Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of user’s content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which user’s situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.
Exploration exploitation trade off in mobile context-aware recommender systemsBouneffouf Djallel
Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, loca-tion, or social aspects. However, none of them have considered the problem of user’s content dynamicity. This problem has been studied in the reinforcement learning community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles the user’s content dynamicity by modeling the CRS as a contextual bandit algorithm. It is based on dynamic explora-tion/exploitation and it includes a metric to decide which user’s situation is the most relevant to exploration or exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demon-strate that our algorithm outperforms surveyed algorithms.
Hybrid-e-greedy for mobile context-aware recommender systemBouneffouf Djallel
This document proposes a hybrid-ε-greedy algorithm for mobile context-aware recommender systems that combines bandit algorithms and case-based reasoning. It summarizes related works that aim to follow the evolution of user interests or manage the user's context, but not both. The proposed approach uses case-based reasoning to consider the user's context in the bandit algorithm's exploration-exploitation strategy. It also uses content-based filtering with the ε-greedy algorithm.
Learning and inference engine applied to ubiquitous recommender systemBouneffouf Djallel
The document discusses a learning and inference engine applied to a ubiquitous recommender system. It aims to help users access information by guiding them based on their context and situation. The system should be able to recommend information to help users achieve their goals. Major challenges include avoiding expert intervention, starting with no prior knowledge, quick learning, and adapting to changing user interests. The document presents a scenario where the recommender system infers relevant information to recommend to new employees without expert input, based on analyzing the actions of other employees in their teams. It aims to start with a predefined set of actions from social groups and progressively adapt recommendations to individual users.
Proactive Intelligent Home System Using Contextual Information and Neural Net...IJERA Editor
Nowadays, cities around the world intend to use information technology to improve the lives of their citizens.
Future smart cities will incorporate digital data and technology to interact differently with their human
inhabitants.
Among the key component of a smart city, we find the smart home component. It is an autonomic environment
that can provide various smart services by considering the user’s context information. Several methods are used
in context-aware system to provide such services. In this paper, we propose an approach to offer the most
relevant services to the user according to any significant change of his context environment. The proposed
approach is based on the use of context history information together with user profiling and machine learning
techniques. Experimentations show that the proposed solution can efficiently provide the most useful services to
the user in an intelligent home environment.
Following the user’s interests in mobile context aware recommender systemsBouneffouf Djallel
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.
DYNAMIC AND REALTIME MODELLING OF UBIQUITOUS INTERACTIONcscpconf
This document discusses modeling real-time interaction between a user and a ubiquitous system using dynamic Petri net models. It proposes using Petri nets to model a user's activity as a set of elementary actions. Elementary actions are modeled as Petri net structures that are then composed together through techniques like sequence, parallelism, etc. to form an overall model of user-system interaction. The models can be dynamically adapted based on changes to the user's context. OWL-S ontology is used to describe the dynamic aspects of the Petri net models, especially real-time composition of models. Simulation results validate the approach of dynamically modeling user-system interaction through mutation of Petri net models.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
This document proposes a novel approach for travel package recommendation using probabilistic matrix factorization (PMF). It discusses how existing recommendation systems are usually classification-based and supervised, whereas the proposed approach uses an unsupervised E-TRAST (Efficient-Tourist Relation Area Season Topic) model. The E-TRAST model represents travel packages and tourists using different topics modeled through PMF. It analyzes travel data characteristics and introduces a cocktail approach considering features like seasonal tourist performance to recommend customized travel packages.
Yifan Guo is a PhD student at Case Western Reserve University studying machine learning and big data. He received his B.S. from Beijing University of Posts and Telecommunications and his Master's from Northwestern University. His research projects include developing an image recognition system for identifying pill types, building a movie recommendation system using matrix factorization, and designing an algorithm for a nonlinear integer programming transportation problem.
Exploration exploitation trade off in mobile context-aware recommender systemsBouneffouf Djallel
Most existing approaches in Context-Aware Recommender Systems (CRS) focus on recommending relevant items to users taking into account contextual information, such as time, loca-tion, or social aspects. However, none of them have considered the problem of user’s content dynamicity. This problem has been studied in the reinforcement learning community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles the user’s content dynamicity by modeling the CRS as a contextual bandit algorithm. It is based on dynamic explora-tion/exploitation and it includes a metric to decide which user’s situation is the most relevant to exploration or exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demon-strate that our algorithm outperforms surveyed algorithms.
Hybrid-e-greedy for mobile context-aware recommender systemBouneffouf Djallel
This document proposes a hybrid-ε-greedy algorithm for mobile context-aware recommender systems that combines bandit algorithms and case-based reasoning. It summarizes related works that aim to follow the evolution of user interests or manage the user's context, but not both. The proposed approach uses case-based reasoning to consider the user's context in the bandit algorithm's exploration-exploitation strategy. It also uses content-based filtering with the ε-greedy algorithm.
Learning and inference engine applied to ubiquitous recommender systemBouneffouf Djallel
The document discusses a learning and inference engine applied to a ubiquitous recommender system. It aims to help users access information by guiding them based on their context and situation. The system should be able to recommend information to help users achieve their goals. Major challenges include avoiding expert intervention, starting with no prior knowledge, quick learning, and adapting to changing user interests. The document presents a scenario where the recommender system infers relevant information to recommend to new employees without expert input, based on analyzing the actions of other employees in their teams. It aims to start with a predefined set of actions from social groups and progressively adapt recommendations to individual users.
Proactive Intelligent Home System Using Contextual Information and Neural Net...IJERA Editor
Nowadays, cities around the world intend to use information technology to improve the lives of their citizens.
Future smart cities will incorporate digital data and technology to interact differently with their human
inhabitants.
Among the key component of a smart city, we find the smart home component. It is an autonomic environment
that can provide various smart services by considering the user’s context information. Several methods are used
in context-aware system to provide such services. In this paper, we propose an approach to offer the most
relevant services to the user according to any significant change of his context environment. The proposed
approach is based on the use of context history information together with user profiling and machine learning
techniques. Experimentations show that the proposed solution can efficiently provide the most useful services to
the user in an intelligent home environment.
Following the user’s interests in mobile context aware recommender systemsBouneffouf Djallel
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.
DYNAMIC AND REALTIME MODELLING OF UBIQUITOUS INTERACTIONcscpconf
This document discusses modeling real-time interaction between a user and a ubiquitous system using dynamic Petri net models. It proposes using Petri nets to model a user's activity as a set of elementary actions. Elementary actions are modeled as Petri net structures that are then composed together through techniques like sequence, parallelism, etc. to form an overall model of user-system interaction. The models can be dynamically adapted based on changes to the user's context. OWL-S ontology is used to describe the dynamic aspects of the Petri net models, especially real-time composition of models. Simulation results validate the approach of dynamically modeling user-system interaction through mutation of Petri net models.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
This document proposes a novel approach for travel package recommendation using probabilistic matrix factorization (PMF). It discusses how existing recommendation systems are usually classification-based and supervised, whereas the proposed approach uses an unsupervised E-TRAST (Efficient-Tourist Relation Area Season Topic) model. The E-TRAST model represents travel packages and tourists using different topics modeled through PMF. It analyzes travel data characteristics and introduces a cocktail approach considering features like seasonal tourist performance to recommend customized travel packages.
Yifan Guo is a PhD student at Case Western Reserve University studying machine learning and big data. He received his B.S. from Beijing University of Posts and Telecommunications and his Master's from Northwestern University. His research projects include developing an image recognition system for identifying pill types, building a movie recommendation system using matrix factorization, and designing an algorithm for a nonlinear integer programming transportation problem.
IRJET- Personalize Travel Recommandation based on Facebook DataIRJET Journal
The document summarizes a proposed system for personalized travel recommendation based on Facebook data. It begins by discussing existing challenges with cold start recommendations and existing recommendation techniques. It then proposes a new framework called Implicit-feedback based Content-aware Collaborative Filtering (ICCF) that incorporates semantic content from social networks to address cold start recommendations without negative sampling. Finally, it evaluates ICCF on a large location-based social network dataset and finds it outperforms existing baselines particularly for cold start scenarios by leveraging user profile information.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses a thesis that uses machine learning algorithms to diagnose mental illness using MRI brain scans. Specifically, it analyzes schizophrenia, bipolar disorder, and healthy control subject data from multiple imaging modalities. It trains and tests eight machine learning classifiers - support vector machines, k-nearest neighbors, logistic regression, naive Bayes, and random forests - on the raw imaging data as well as data transformed through dimensionality reduction techniques. The results aim to demonstrate the efficacy of these algorithms at classifying subjects based on their brain scans and diagnosing their mental condition.
This document summarizes a PhD defense presentation on an observer/controller and ontology/rule-based architecture for context-aware pervasive computing systems. The presentation introduces ubiquitous/pervasive computing and discusses context-aware computing approaches. It proposes a new generic context model called Ubiq-OntoRule-CM that uses an ontology for context modeling and rules for context reasoning and service selection. A new generic architecture is also proposed that supports context detection, rapid adaptation, and context modeling/reasoning techniques. The architecture applies an observer/controller concept from organic computing to supervise adaptive systems.
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.
By Alex Egg, accepted to Nvidia GTC 2021 Conference
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEMIAEME Publication
Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature extraction and classification technique for emotion recognition is still an open problem. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. In this paper, the image pre-processing techniques like K-Nearest Neighbor, Cultural Algorithm and Genetic Algorithm are used to remove the noise in the facial image for enhancing the emotion recognition. The performance of the preprocessing techniques are evaluated with various performance metrics.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
This document describes a new approach to machine learning that harnesses the wisdom of crowds to develop predictive models of behavioral outcomes. The approach uses a web platform where users answer questions to predict an outcome (like electricity usage or BMI) and also generate new questions. As more users contribute data by answering questions, models are developed that can better predict outcomes based on the question responses. Two experiments accurately predicted monthly electricity usage and BMI based on models developed from questions crowdsourced by users. This novel approach may lead to new insights into causal factors of behaviors.
The document discusses shilling attacks on recommender systems. It notes that while recommender systems help users find relevant information, they are vulnerable to shilling attacks where malicious users insert biased data to influence recommendations. Different types of attacks aim to increase recommendations for targeted items (push attacks) or decrease recommendations (nuke attacks). The document evaluates the effectiveness of various attack models on user-user and item-item collaborative filtering algorithms. It is found that attacks are more effective on item-item algorithms and for new, low-information items. The document concludes by discussing metrics to potentially detect shilling attacks and improve the security of recommender systems.
Learning Process Interaction Aided by an Adapter Agentpaperpublications3
This document describes a model for improving educator-learner interaction during the learning process using an adaptive system and agents. The model aims to reduce distractions by dynamically adapting content based on the learner's performance, which is monitored by an Exhibition-Module Adapter agent using type-2 fuzzy logic. The model was validated using a case study of an interactive children's museum, where a User agent represents the learner and interacts with a Domain agent that provides adapted content based on the Exhibition-Module Adapter agent's fuzzy perceptions of the learner's performance. The adaptive system and agents are formally defined using a BDI framework with fuzzy perceptions to handle uncertainty.
Using user personalized ontological profile to infer semantic knowledge for p...Joao Luis Tavares
The document proposes a new method for constructing personalized ontological profiles (POP) for users based on their interests and views. It introduces six types of semantic relations between concepts and uses these relations to group related concepts in a user's profile into either general groups or specific groups. It then describes a method for computing the strength of each group based on how semantically related the concepts in the group are, and using this strength to enhance the importance of concepts within strong groups. The approach aims to better model each user's perspective and infer additional interests from their stated preferences.
IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST...csandit
The growing population of elders in the society calls for a new approach in care giving. By
inferring what activities elderly are performing in their houses it is possible to determine their
physical and cognitive capabilities. In this paper we show the potential of important
discriminative classifiers namely the Soft-Support Vector Machines (C-SVM), Conditional
Random Fields (CRF) and k-Nearest Neighbors (k-NN) for recognizing activities from sensor
patterns in a smart home environment. We address also the class imbalance problem in activity
recognition field which has been known to hinder the learning performance of classifiers. Cost
sensitive learning is attractive under most imbalanced circumstances, but it is difficult to
determine the precise misclassification costs in practice. We introduce a new criterion for
selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four
real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed
criterion outperforms the state-of-the-art discriminative methods in activity recognition.
Thesis Blurb intro-Multisensory Warning Cue Evaluation in Driving-Jan18Yuanjing Sun
Multisensory cues can facilitate or impair driving performance depending on their congruency. The document proposes an experiment to test this using a lane change test. It involves presenting visual lane change cues with concurrent auditory cues varying in spatial, temporal and semantic congruency. Response times will be measured to see how congruent and incongruent multisensory cues impact driving performance compared to visual-only cues. The results could help understand how to best design in-vehicle multimodal displays.
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
Integrated bio-search approaches with multi-objective algorithms for optimiza...TELKOMNIKA JOURNAL
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
The document presents an introduction to the concept of Organic Information Design, which aims to create dynamic visualizations of changing data sources. It discusses how organic systems can provide a framework for visualizing complex and dynamic information. Key points:
- Existing techniques for visualizing data are insufficient for dynamic data sources that are continually changing.
- Organic Information Design draws from properties of decentralized organic systems like growth, adaptation and response to stimuli to create interactive visualizations ("Organic Information Visualizations").
- These visualizations aim to depict qualitative features of large, changing datasets to aid understanding, rather than focus on individual data points.
- Psychological responses to the behavior of the visualizations can provide a way to interpret
Matching GPS Traces with Personal
Schedules,” Proc. First ACM Int’l Workshop
Personalized Context Modeling and
Management for UbiComp Applications
(PCM), 2009.
[8] X. Li, Y.-Y. Chen, T. Suel, and A.
Markowetz, “Efficient Query Processing in
Geographic Web Search,” Proc. Int’l ACM
SIGIR Conf. Research and Development in
Information Retrieval (SIGIR), 2006.
[9] B.J. Jansen, A. Spink, and T. Saracevic,
“Real Life, Real Users, and Real Needs: A
Study and Analysis of User Queries
Ontological and clustering approach for content based recommendation systemsvikramadityajakkula
This document proposes a novel content-based recommendation system that uses ontological graphs and dynamic weighted ranking. It builds an adaptive ranking mechanism based on user selections and preferences to improve recommendation accuracy over time. The system segments data into ontological groups and identifies relationships between entities. It then calculates similarity between entities using feature vectors and ranks entities based on weights assigned to their connections in the ontological graph. These weights are updated dynamically based on user feedback to personalize recommendations for each user. The paper describes testing this approach in a recipe recommendation tool called RecipeMiner, which produced coherent recommendations that adapted to user preferences.
A Research Platform for Coevolving Agents.docbutest
The document describes a research platform for coevolving software agents that interact in a producer/consumer economic world. The platform allows agents to evolve strategies for allocating resources to different production technologies and maximize profits. It provides a controlled environment for examining emergent behaviors from coevolution and how system parameters affect those behaviors. The design uses object-oriented classes like producerAgent and marketAgent to represent the agents and economic rules in a modular, extensible way for ongoing experiments.
A Research Platform for Coevolving Agents.docbutest
The document describes a research platform for coevolving software agents that interact in a producer/consumer economic world. The platform allows agents to evolve strategies for allocating resources to different production technologies and maximize profits. It provides a controlled environment for examining emergent behaviors from coevolution and how system parameters affect those behaviors. The platform uses an extensible object-oriented design with key classes including market agents that facilitate trade, an economic world class defining market rules, and producer agents that determine production strategies and breed new generations of agents.
A Research Platform for Coevolving Agents.docbutest
This document discusses a research platform for studying coevolving agents that interact in a producer/consumer economic world. The platform allows agents to evolve using evolutionary computation techniques. The motivations for using evolutionary computation to enable agent adaptation are discussed, including empirical evidence that complex cooperative behaviors can emerge from coevolved rulesets. Additionally, Holland's work on adaptation in natural systems provides theoretical justification for using evolutionary computation to propagate advantageous features through a distributed system of agents.
IRJET- Personalize Travel Recommandation based on Facebook DataIRJET Journal
The document summarizes a proposed system for personalized travel recommendation based on Facebook data. It begins by discussing existing challenges with cold start recommendations and existing recommendation techniques. It then proposes a new framework called Implicit-feedback based Content-aware Collaborative Filtering (ICCF) that incorporates semantic content from social networks to address cold start recommendations without negative sampling. Finally, it evaluates ICCF on a large location-based social network dataset and finds it outperforms existing baselines particularly for cold start scenarios by leveraging user profile information.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses a thesis that uses machine learning algorithms to diagnose mental illness using MRI brain scans. Specifically, it analyzes schizophrenia, bipolar disorder, and healthy control subject data from multiple imaging modalities. It trains and tests eight machine learning classifiers - support vector machines, k-nearest neighbors, logistic regression, naive Bayes, and random forests - on the raw imaging data as well as data transformed through dimensionality reduction techniques. The results aim to demonstrate the efficacy of these algorithms at classifying subjects based on their brain scans and diagnosing their mental condition.
This document summarizes a PhD defense presentation on an observer/controller and ontology/rule-based architecture for context-aware pervasive computing systems. The presentation introduces ubiquitous/pervasive computing and discusses context-aware computing approaches. It proposes a new generic context model called Ubiq-OntoRule-CM that uses an ontology for context modeling and rules for context reasoning and service selection. A new generic architecture is also proposed that supports context detection, rapid adaptation, and context modeling/reasoning techniques. The architecture applies an observer/controller concept from organic computing to supervise adaptive systems.
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.
By Alex Egg, accepted to Nvidia GTC 2021 Conference
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEMIAEME Publication
Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature extraction and classification technique for emotion recognition is still an open problem. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. In this paper, the image pre-processing techniques like K-Nearest Neighbor, Cultural Algorithm and Genetic Algorithm are used to remove the noise in the facial image for enhancing the emotion recognition. The performance of the preprocessing techniques are evaluated with various performance metrics.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
This document describes a new approach to machine learning that harnesses the wisdom of crowds to develop predictive models of behavioral outcomes. The approach uses a web platform where users answer questions to predict an outcome (like electricity usage or BMI) and also generate new questions. As more users contribute data by answering questions, models are developed that can better predict outcomes based on the question responses. Two experiments accurately predicted monthly electricity usage and BMI based on models developed from questions crowdsourced by users. This novel approach may lead to new insights into causal factors of behaviors.
The document discusses shilling attacks on recommender systems. It notes that while recommender systems help users find relevant information, they are vulnerable to shilling attacks where malicious users insert biased data to influence recommendations. Different types of attacks aim to increase recommendations for targeted items (push attacks) or decrease recommendations (nuke attacks). The document evaluates the effectiveness of various attack models on user-user and item-item collaborative filtering algorithms. It is found that attacks are more effective on item-item algorithms and for new, low-information items. The document concludes by discussing metrics to potentially detect shilling attacks and improve the security of recommender systems.
Learning Process Interaction Aided by an Adapter Agentpaperpublications3
This document describes a model for improving educator-learner interaction during the learning process using an adaptive system and agents. The model aims to reduce distractions by dynamically adapting content based on the learner's performance, which is monitored by an Exhibition-Module Adapter agent using type-2 fuzzy logic. The model was validated using a case study of an interactive children's museum, where a User agent represents the learner and interacts with a Domain agent that provides adapted content based on the Exhibition-Module Adapter agent's fuzzy perceptions of the learner's performance. The adaptive system and agents are formally defined using a BDI framework with fuzzy perceptions to handle uncertainty.
Using user personalized ontological profile to infer semantic knowledge for p...Joao Luis Tavares
The document proposes a new method for constructing personalized ontological profiles (POP) for users based on their interests and views. It introduces six types of semantic relations between concepts and uses these relations to group related concepts in a user's profile into either general groups or specific groups. It then describes a method for computing the strength of each group based on how semantically related the concepts in the group are, and using this strength to enhance the importance of concepts within strong groups. The approach aims to better model each user's perspective and infer additional interests from their stated preferences.
IMPROVING SUPERVISED CLASSIFICATION OF DAILY ACTIVITIES LIVING USING NEW COST...csandit
The growing population of elders in the society calls for a new approach in care giving. By
inferring what activities elderly are performing in their houses it is possible to determine their
physical and cognitive capabilities. In this paper we show the potential of important
discriminative classifiers namely the Soft-Support Vector Machines (C-SVM), Conditional
Random Fields (CRF) and k-Nearest Neighbors (k-NN) for recognizing activities from sensor
patterns in a smart home environment. We address also the class imbalance problem in activity
recognition field which has been known to hinder the learning performance of classifiers. Cost
sensitive learning is attractive under most imbalanced circumstances, but it is difficult to
determine the precise misclassification costs in practice. We introduce a new criterion for
selecting the suitable cost parameter C of the C-SVM method. Through our evaluation on four
real world imbalanced activity datasets, we demonstrate that C-SVM based on our proposed
criterion outperforms the state-of-the-art discriminative methods in activity recognition.
Thesis Blurb intro-Multisensory Warning Cue Evaluation in Driving-Jan18Yuanjing Sun
Multisensory cues can facilitate or impair driving performance depending on their congruency. The document proposes an experiment to test this using a lane change test. It involves presenting visual lane change cues with concurrent auditory cues varying in spatial, temporal and semantic congruency. Response times will be measured to see how congruent and incongruent multisensory cues impact driving performance compared to visual-only cues. The results could help understand how to best design in-vehicle multimodal displays.
Friend Recommendation on Social Network Site Based on Their Life Stylepaperpublications3
Abstract: Social network sites attracted millions of users. In the social network sites, a user can register other users as friends and enjoy communication. Existing social networking sites recommend friends to users based on their social graphs, which may not be appropriate. In proposed system friends recommends to users based on their life styles instead of social graphs. It done by means of sensor rich smart- phone serve as the ideal platform for sensing daily routines from which people’s life styles could be discovered. Unsupervised learning method is used. Achieve an efficient activity Recognition and reduce the false positive of Friend Recommendation. Friendbook integrates a feedback mechanism. Finally the results show that the recommendations accurately reflect the preferences of users in choosing friends.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
Integrated bio-search approaches with multi-objective algorithms for optimiza...TELKOMNIKA JOURNAL
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
The document presents an introduction to the concept of Organic Information Design, which aims to create dynamic visualizations of changing data sources. It discusses how organic systems can provide a framework for visualizing complex and dynamic information. Key points:
- Existing techniques for visualizing data are insufficient for dynamic data sources that are continually changing.
- Organic Information Design draws from properties of decentralized organic systems like growth, adaptation and response to stimuli to create interactive visualizations ("Organic Information Visualizations").
- These visualizations aim to depict qualitative features of large, changing datasets to aid understanding, rather than focus on individual data points.
- Psychological responses to the behavior of the visualizations can provide a way to interpret
Matching GPS Traces with Personal
Schedules,” Proc. First ACM Int’l Workshop
Personalized Context Modeling and
Management for UbiComp Applications
(PCM), 2009.
[8] X. Li, Y.-Y. Chen, T. Suel, and A.
Markowetz, “Efficient Query Processing in
Geographic Web Search,” Proc. Int’l ACM
SIGIR Conf. Research and Development in
Information Retrieval (SIGIR), 2006.
[9] B.J. Jansen, A. Spink, and T. Saracevic,
“Real Life, Real Users, and Real Needs: A
Study and Analysis of User Queries
Ontological and clustering approach for content based recommendation systemsvikramadityajakkula
This document proposes a novel content-based recommendation system that uses ontological graphs and dynamic weighted ranking. It builds an adaptive ranking mechanism based on user selections and preferences to improve recommendation accuracy over time. The system segments data into ontological groups and identifies relationships between entities. It then calculates similarity between entities using feature vectors and ranks entities based on weights assigned to their connections in the ontological graph. These weights are updated dynamically based on user feedback to personalize recommendations for each user. The paper describes testing this approach in a recipe recommendation tool called RecipeMiner, which produced coherent recommendations that adapted to user preferences.
A Research Platform for Coevolving Agents.docbutest
The document describes a research platform for coevolving software agents that interact in a producer/consumer economic world. The platform allows agents to evolve strategies for allocating resources to different production technologies and maximize profits. It provides a controlled environment for examining emergent behaviors from coevolution and how system parameters affect those behaviors. The design uses object-oriented classes like producerAgent and marketAgent to represent the agents and economic rules in a modular, extensible way for ongoing experiments.
A Research Platform for Coevolving Agents.docbutest
The document describes a research platform for coevolving software agents that interact in a producer/consumer economic world. The platform allows agents to evolve strategies for allocating resources to different production technologies and maximize profits. It provides a controlled environment for examining emergent behaviors from coevolution and how system parameters affect those behaviors. The platform uses an extensible object-oriented design with key classes including market agents that facilitate trade, an economic world class defining market rules, and producer agents that determine production strategies and breed new generations of agents.
A Research Platform for Coevolving Agents.docbutest
This document discusses a research platform for studying coevolving agents that interact in a producer/consumer economic world. The platform allows agents to evolve using evolutionary computation techniques. The motivations for using evolutionary computation to enable agent adaptation are discussed, including empirical evidence that complex cooperative behaviors can emerge from coevolved rulesets. Additionally, Holland's work on adaptation in natural systems provides theoretical justification for using evolutionary computation to propagate advantageous features through a distributed system of agents.
Modeling the Adaption Rule in Contextaware Systemsijasuc
Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing
systems. Each context-aware application has its own set of behaviors to react to context modifications. This
paper is concerned with the context modeling and the development methodology for context-aware systems.
We proposed a rule-based approach and use the adaption tree to model the adaption rule of context-aware
systems. We illustrate this idea in an arithmetic game application.
MODELING THE ADAPTION RULE IN CONTEXTAWARE SYSTEMSijasuc
Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing
systems. Each context-aware application has its own set of behaviors to react to context modifications. This
paper is concerned with the context modeling and the development methodology for context-aware systems.
We proposed a rule-based approach and use the adaption tree to model the adaption rule of context-aware
systems. We illustrate this idea in an arithmetic game application.
Temporal Reasoning Graph for Activity RecognitionIRJET Journal
This document discusses using a convolutional neural network and background subtraction for human activity recognition in videos. It proposes a model that uses CNN to extract features from video frames and classify human activities. The proposed system first acquires and preprocesses video data. It then extracts frames from the videos using background subtraction. These frames are split into training and testing sets for the CNN model. The CNN model is tested on the testing set to evaluate its ability to accurately classify human activities. Experimental results show the CNN model combined with background subtraction achieves good performance for human activity recognition.
The document discusses the differences between machine learning (ML), statistical learning, data mining (DM), and automated learning (AL). It argues that while ML and statistical learning developed similar techniques starting in the 1960s, DM emerged in the 1990s from a merging of database research and automated learning. However, industry was much more enthusiastic about adopting DM techniques compared to AL techniques, even though many DM systems are just friendly interfaces of AL systems. The document aims to explain the key differences between DM and AL that led to DM's greater commercial success.
Inspection of Certain RNN-ELM Algorithms for Societal ApplicationsIRJET Journal
The document proposes a hybrid Recurrent Neural Network (RNN)-Extreme Learning Machine (ELM) model for crime classification using crime data from Philadelphia. The RNN extracts relevant features from the data using a Long Short-Term Memory (LSTM) model and learns patterns over time. The ELM is then applied at the end for the classification task. The proposed model is evaluated on accuracy, precision, and recall and is found to perform crime classification without backpropagation, making it faster than traditional neural networks. The model could help law enforcement identify crime hotspots and adjust policing strategies based on the predicted crime rates at different locations and times.
Army Study: Ontology-based Adaptive Systems of Cyber DefenseRDECOM
The U.S. Army Research Laboratory is part of the U.S. Army Research, Development and Engineering Command, which has the mission to ensure decisive overmatch for unified land operations to empower the Army, the joint warfighter and our nation. RDECOM is a major subordinate command of the U.S. Army Materiel Command.
A comprehensive review of the firefly algorithmsXin-She Yang
This document provides a comprehensive review of firefly algorithms. It begins with background on swarm intelligence and how firefly algorithms were inspired by the flashing lights of fireflies. It then describes the basic structure of firefly algorithms, including initializing a population of fireflies, evaluating their fitness, sorting by fitness, selecting the best solution, and moving fireflies toward more attractive solutions over generations. The document reviews applications of firefly algorithms in areas like continuous, combinatorial, and multi-objective optimization as well as engineering problems. It concludes by discussing exploration vs exploitation in firefly algorithms and directions for further development.
This summarizes a research poster presentation on an unsupervised machine learning framework to learn and predict individual daily activity patterns for personal robots. The framework uses a 2-layer LDA model to classify daily activity data from sensors into topics and extract features. It then applies random classification and regression forests to the LDA outputs to learn patterns and predict activities. An experiment applying this framework to one user's 3 months of indoor location and app usage data achieved an average 65.6% F-measure for activity prediction and 83.5% precision for frequent activities.
The document summarizes five papers that address challenges in context-aware recommendation systems using factorization methods. Three key challenges are high dimensionality, data sparsity, and cold starts. The papers propose various algorithms using matrix factorization and tensor factorization to address these challenges. COT models each context as an operation on user-item pairs to reduce dimensionality. Another approach extracts latent contexts from sensor data using deep learning and matrix factorization. CSLIM extends the SLIM algorithm to incorporate contextual ratings. TAPER uses tensor factorization to integrate various contexts for expert recommendations. Finally, GFF provides a generalized factorization framework to handle different recommendation models. The document analyzes how well each paper meets the challenges.
Multi-Agent System (MAS) monitoring solutions are designed for a plethora of usage topics. Existing approach mostly used cloned back-end architectures while front-end monitoring interface tends to constitute the real specificity of the solution. These interfaces are recurrently structured around three dimensions: access to informed knowledge, agent’s behavioural rules, and restitution of real-time states of specific system sector. In this paper, we propose prototyping a sector-agnostic MAS platform (Smart-X) which gathers in an integrated and independent platform all the functionalities required to monitor and to govern a wide range of sector specific environments. For illustration and validation purposes, the use of Smart-X is introduced and explained with a smart-mobility case study.
A LIGHT-WEIGHT DISTRIBUTED SYSTEM FOR THE PROCESSING OF REPLICATED COUNTER-LI...ijdpsjournal
In order to increase availability in a distributed system some or all of the data items are replicated and
stored at separate sites. This is an issue of key concern especially since there is such a proliferation of
wireless technologies and mobile users. However, the concurrent processing of transactions at separate
sites can generate inconsistencies in the stored information. We have built a distributed service that
manages updates to widely deployed counter-like replicas. There are many heavy-weight distributed
systems targeting large information critical applications. Our system is intentionally, relatively lightweight
and useful for the somewhat reduced information critical applications. The service is built on our
distributed concurrency control scheme which combines optimism and pessimism in the processing of
transactions. The service allows a transaction to be processed immediately (optimistically) at any
individual replica as long as the transaction satisfies a cost bound. All transactions are also processed in a
concurrent pessimistic manner to ensure mutual consistency
Paper Gloria Cea - Goal-Oriented Design Methodology Applied to User Interface...WTHS
This document describes a user interface designed for a mobile application called the Functional Assessment System (FAS). The FAS allows users to assess their aerobic fitness on their own without specialized equipment. The design of the mobile application interface was guided by the Goal-Oriented Design methodology. This methodology focuses on representing users as characters with specific goals and designing scenarios to help users achieve those goals. The document also discusses evaluating the usability of the interface using the AttrakDiff questionnaire to assess pragmatic and hedonic qualities. The results showed satisfactory user interaction with the FAS mobile application interface.
A Novel Collaborative Filtering Algorithm by Bit Mining Frequent ItemsetsLoc Nguyen
Collaborative filtering (CF) is a popular technique in recommendation study. Concretely, items which are recommended to user are determined by surveying her/his communities. There are two main CF approaches, which are memory-based and model-based. I propose a new CF model-based algorithm by mining frequent itemsets from rating database. Hence items which belong to frequent itemsets are recommended to user. My CF algorithm gives immediate response because the mining task is performed at offline process-mode. I also propose another so-called Roller algorithm for improving the process of mining frequent itemsets. Roller algorithm is implemented by heuristic assumption “The larger the support of an item is, the higher it’s likely that this item will occur in some frequent itemset”. It models upon doing white-wash task, which rolls a roller on a wall in such a way that is capable of picking frequent itemsets. Moreover I provide enhanced techniques such as bit representation, bit matching and bit mining in order to speed up recommendation process. These techniques take advantages of bitwise operations (AND, NOT) so as to reduce storage space and make algorithms run faster.
The document describes a study that investigates using gestures as a form of authentication on smartwatches. The researchers collected accelerometer data from smartwatches as users performed different gestures. They extracted time and frequency domain features from the data and used k-nearest neighbors and random forest classifiers to distinguish between gestures and identify individual users performing the same gesture. Through 5-fold cross validation experiments, they found it was possible to accurately classify gestures and identify users with error rates comparable or better than previous gait-based authentication studies. This suggests gesture-based authentication on smartwatches is a viable solution.
The social dynamics of software developmentaliaalistartup
This document summarizes a study examining the software procurement processes between a university and several software vendors over a decade. It presents three case studies of information systems development histories and analyzes them using a social process model to depict how relationships evolved over time. Major events that changed the relationship between parties were identified as "encounters", with stable periods in between labeled "episodes". While traditional models view procurement statically, this longitudinal study revealed the dynamic nature of procurement strategies over time in the case studies.
A HUMAN-CENTRIC APPROACH TO GROUP-BASED CONTEXT-AWARENESSIJNSA Journal
The emerging need for qualitative approaches in context-aware information processing calls for proper modelling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modelling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendibility remains limited. In this paper, we present f-Context as a service-based contextawareness framework, based on language-action perspective (LAP) theory for modelling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems.
Similar to A contextual bandit algorithm for mobile context-aware recommender system (20)
2. has to explore documents by choosing seemingly suboptimal documents so as to
gather more information about them. Exploitation can decrease short-term user’s sat-
isfaction since some suboptimal documents may be chosen. However, obtaining in-
formation about the documents’ average rewards (i.e., exploration) can refine B’s
estimate of the documents’ rewards and in turn increases long-term user’s satisfac-
tion. Clearly, neither a purely exploring nor a purely exploiting algorithm works well,
and a good tradeoff is needed. One classical solution to the multi-armed bandit prob-
lem is the ε-greedy strategy [12]. With the probability 1-ε, this algorithm chooses the
best documents based on current knowledge; and with the probability ε, it uniformly
chooses any other documents uniformly. The ε parameter controls essentially the
exp/exr tradeoff between exploitation and exploration. One drawback of this algo-
rithm is that it is difficult to decide in advance the optimal value. Instead, we intro-
duce an algorithm named Contextual-ε-greedy that achieves this goal by balancing
adaptively the exp/exr tradeoff according to the user’s situation. This algorithm ex-
tends the ε-greedy strategy with an update of the exr/exp-tradeoff by selecting suitable
user’s situations for either exploration or exploitation.
The rest of the paper is organized as follows. Section 2 gives the key notions used
throughout this paper. Section 3 reviews some related works. Section 4 presents our
MCRS model and describes the algorithms involved in the proposed approach. The
experimental evaluation is illustrated in Section 5. The last section concludes the pa-
per and points out possible directions for future work.
2 Key Notions
In this section, we briefly sketch the key notions that will be of use in this paper.
The user’s model: The user’s model is structured as a case based, which is composed
of a set of situations with their corresponding user’s preferences, denoted U = {(S i;
UPi)}, where Si is the user’s situation and UPi its corresponding user’s preferences.
The user’s preferences: The user’s preferences are deduced during the user’s naviga-
tion activities, for example the number of clicks on the visited documents or the time
spent on a document. Let UP be the preferences submitted by a specific user in the
system at a given situation. Each document in UP is represented as a single vector
d=(c1,...,cn), where ci (i=1, .., n) is the value of a component characterizing the prefer-
ences of d. We consider the following components: the total number of clicks on d,
the total time spent reading d and the number of times d was recommended.
Context: A user’s context C is a multi-ontology representation where each ontology
corresponds to a context dimension C=(OLocation, OTime, OSocial). Each dimension mod-
els and manages a context information type. We focus on these three dimensions since
they cover all needed information. These ontologies are described in [1, 16].
Situation: A situation is an instantiation of the user’s context. We consider a situation
as a triple S = (OLocation.xi, OTime.xj, OSocial.xk) where xi, xj and xk are ontology concepts
or instances. Suppose the following data are sensed from the user’s mobile phone: the
GPS shows the latitude and longitude of a point "48.89, 2.23"; the local time is
"Oct_3_12:10_2012" and the calendar states "meeting with Paul Gerard". The corre-
sponding situation is: S=("48.89,2.23","Oct_3_12:10_2012","Paul_Gerard"). To build
3. a more abstracted situation, we interpret the user’s behavior from this low-level multi-
modal sensor data using ontologies reasoning means. For example, from S, we obtain
the following situation: Meeting=(Restaurant, Work_day, Financial_client).
Among the set of captured situations, some of them are characterized as High-Level
Critical Situations.
High-Level Critical Situations (HLCS): A HLCS is a class of situations where the
user needs the best information that can be recommended by the system, for instance,
during a professional meeting. In such a situation, the system must exclusively perform
exploitation rather than exploration-oriented learning. In the other case, where the user
is for instance using his/her information system at home, on vacation with friends, the
system can make some exploration by recommending some information ignoring
his/her interest. The HLCS are predefined by the domain expert. In our case we con-
duct the study with professional mobile users, which is described in detail in Section 5.
As examples of HLCS, we can find S1 = (restaurant, midday, client) or S2= (company,
morning, manager).
3 Related Work
We refer, in the following, recent recommendation techniques that tackle the problem
of making dynamic exr/exp (bandit algorithms). Existing works considering the user’s
situation in recommendation are not considered in this section, refer to [1] for further
information.
Very frequently used in reinforcement learning to study the exr/exp tradeoff, the mul-
ti-armed bandit problem was originally described by Robbins [11]. The ε-greedy is
one of the most used strategy to solve the bandit problem and was first described in
[10]. The ε-greedy strategy chooses a random document with epsilon-frequency (ε),
and chooses the document with the highest estimated mean otherwise. The estimation
is based on the rewards observed thus far. ε must be in the interval [0, 1] and its
choice is left to the user. The first variant of the ε-greedy strategy is what [6, 10] refer
to as the ε-beginning strategy. This strategy makes exploration all at once at the be-
ginning. For a given number I of iterations, documents are randomly pulled during the
εI first iterations; during the remaining (1−ε)I iterations, the document of highest
estimated mean is pulled. Another variant of the ε-greedy strategy is what [10] calls
the ε-decreasing. In this strategy, the document with the highest estimated mean is
always pulled except when a random document is pulled instead with εi frequency,
where εi = {ε0/ i}, ε0 ∈]0,1] and i is the index of the current round. Besides ε-
decreasing, four other strategies presented [3]. Those strategies are not described here
because the experiments done by [3] seem to show that ε-decreasing is always as
good as the other strategies. Compared to the standard multi-armed bandit problem
with a fixed set of possible actions, in MCRS, old documents may expire and new
documents may frequently emerge. Therefore it may not be desirable to perform the
exploration all at once at the beginning as in [6] or to decrease monotonically the
effort on exploration as the decreasing strategy in [10].
As far as we know, no existing works address the problem of exr/exp tradeoff in
MCRS. However few research works are dedicated to study the contextual bandit
problem on recommender systems, where they consider the user’s behavior as the
4. context of the bandit problem. In [13], the authors extend the ε-greedy strategy by
dynamically updating the ε exploration value. At each iteration, they run a sampling
procedure to select a new ε from a finite set of candidates. The probabilities associat-
ed to the candidates are uniformly initialized and updated with the Exponentiated
Gradient (EG) [7]. This updating rule increases the probability of a candidate ε if it
leads to a user’s click. Compared to both ε-beginning and ε-decreasing, this technique
gives better results. In [9], authors model the recommendation as a contextual bandit
problem. They propose an approach in which a learning algorithm sequentially selects
documents to serve users based on their behavior. To maximize the total number of
user’s clicks, this work proposes LINUCB algorithm that is computationally efficient.
As shown above, none of the mentioned works tackles both problems of exr/exp
dynamicity and user’s situation consideration in the exr/exp strategy. This is precisely
what we intend to do with our approach. Our intuition is that, considering the criticali-
ty of the situation when managing the exr/exp-tradeoff, improves the result of the
MCRS. This strategy achieves high exploration when the current user’s situation is
not critical and achieves high exploitation in the inverse case.
4 MCRS Model
In our recommender system, the recommendation of documents is modeled as a con-
textual bandit problem including user’s situation information [8]. Formally, a bandit
algorithm proceeds in discrete trials t = 1…T. For each trial t, the algorithm performs
the following tasks:
Task 1: Let S t be the current user’s situation, and PS the set of past situations.
The system compares S t with the situations in PS in order to choose the most
similar one, S p:
S p= arg max sim( S t , S c ) (1)
Sc PS
The semantic similarity metric is computed by:
sim(S t ,S c ) = j sim j x tj ,x c
j (2)
j
In Eq.2, simj is the similarity metric related to dimension j between two concepts
xjt and xjc; αj is the weight associated to dimension j (during the experimental
phase, αj has a value of 1 for all dimensions). This similarity depends on how
closely xj c and xjc are related in the corresponding ontology. We use the same
similarity measure as [15, 17, 18] defined by:
sim j x tj , x c 2
deph( LCS ) (3)
(deph( x c ) deph( x tj ))
j
j
In Eq. 3, LCS is the Least Common Subsumer of xjt and xjc, and deph is the
number of nodes in the path from the node to the ontology root.
Task 2: Let D be the document collection and Dp D the set of documents rec-
5. ommended in situation S p. After retrieving S p, the system observes the user’s
behavior when reading each document d p Dp. Based on observed rewards, the
algorithm chooses document d p with the greater reward r p.
Task 3: After receiving the user ’s reward, the algorithm improves its document-
selection strategy with the new observation: in situation S t, document d p obtains
a reward rt.
When a document is presented to the user and this one selects it by a click, a
reward of 1 is incurred; otherwise, the reward is 0. The reward of a document is
precisely its Click Through Rate (CTR). The CTR is the average number of
clicks on a document by recommendation.
4.1 The ε-greedy algorithm
The ε-greedy algorithm recommends a predefined number of documents N selected
using the following equation:
di argmax UC ( getCTR( d ))
Random(UC )
if ( q )
otherwise
(4)
In Eq. 4, i∈{1,…N}, UC={d1,…,dP} is the set of documents corresponding to the
user’s preferences; getCTR() computes the CTR of a given document; Random() re-
turns a random element from a given set, allowing to perform exploration; q is a ran-
dom value uniformly distributed over [0, 1] which defines the exr/exp tradeoff; ε is
the probability of recommending a random exploratory document.
4.2 T h e contextual-ε-greedy algorithm
To improve the adaptation of the ε-greedy algorithm to HLCS situations, the
contextual-ε-greedy algorithm compares the current user’s situation St with the HLCS
class of situations. Depending on the similarity between the St and its most similar
situation Sm ∈ HLCS, being B the similarity threshold (this metric is discussed below),
two scenarios are possible:
(1) If sim(St, Sm) ≥ B, the current situation is critical; the ε-greedy algorithm is used
with ε=0 (exploitation) and St is inserted in the HLCS class of situations.
(2) If sim(St, Sm) < B, the current situation is not critical; the ε-greedy algorithm is
used with ε>0 (exploration) computed as indicated in Eq.5.
sim( S t , S m )
1 if ( sim( S t , S m )) B
B (5)
0 otherwise
To summarize, the system does not make exploration when the current user’s situa-
tion is critical; otherwise, the system performs exploration. In this case, the degree of
exploration decreases when the similarity between St and Sm increases.
6. 5 Experimental Evaluation
In order to empirically evaluate the performance of our approach, and in the absence
of a standard evaluation framework, we propose an evaluation framework based on a
diary set of study entries. The main objectives of the experimental evaluation are: (1)
to find the optimal threshold B value described in Section 4.2 and (2) to evaluate the
performance of the proposed algorithm (contextual-ε-greedy). In the following, we
describe our experimental datasets and then present and discuss the obtained results.
We have conducted a diary study with the collaboration of the French software
company Nomalys1. This company provides a history application, which records the
time, current location, social and navigation information of its users during their ap-
plication use. The diary study has taken 18 months and has generated 178 369 diary
situation entries. Each diary situation entry represents the capture, of contextual time,
location and social information. For each entry, the captured data are replaced with
more abstracted information using time, spatial and social ontologies [1]. From the
diary study, we have obtained a total of 2 759 283 entries concerning the user’s navi-
gation, expressed with an average of 15.47 entries per situation.
In order to set out the threshold similarity value, we use a manual classification as
a baseline and compare it with the results obtained by our technique. So, we take a
random sampling of 10% of the situation entries, and we manually group similar situ-
ations; then we compare the constructed groups with the results obtained by our simi-
larity algorithm, with different threshold values.
Fig. 1. Effect of B threshold value on the similarity precision
Fig. 1 shows the effect of varying the threshold situation similarity parameter B in the
interval [0, 3] on the overall precision. Results show that the best performance is ob-
tained when B has the value 2.4 achieving a precision of 0.849. Consequently, we use
the optimal threshold value B = 2.4 for testing our MCRS.
To test the proposed contextual-ε-greedy algorithm, we firstly have collected 3000
situations with an occurrence greater than 100 to be statistically meaningful. Then, we
1
Nomalys is a company that provides a graphical application on Smartphones allowing users to
access their company’s data.
7. have sampled 10000 documents that have been shown on any of these situations. The
testing step consists of evaluating the algorithms for each testing situation using the
average CTR. The average CTR for a particular iteration is the ratio between the total
number of clicks and the total number of displays. Then, we calculate the average
CTR over every 1000 iterations. The number of documents (N) returned by the rec-
ommender system for each situation is 10 and we have run the simulation until the
number of iterations reaches 10000, which is the number of iterations where all algo-
rithms have converged. In the first experiment, in addition to a pure exploitation base-
line, we have compared our algorithm to the algorithms described in the related work
(Section 3): ε-greedy; ε-beginning, ε-decreasing and EG. In Fig. 2, the horizontal axis
is the number of iterations and the vertical axis is the performance metric.
Fig. 2. Average CTR for exr/exp algorithms
We have parameterized the different algorithms as follows: ε-greedy was tested with
two parameter values: 0.5 and 0.9; ε-decreasing and EG use the same set {εi = 1- 0.01
* i, i = 1,...,100}; ε-decreasing starts using the highest value and reduces it by 0.01
every 100 iterations, until it reaches the smallest value. Overall tested algorithms
have better performance than the baseline. However, for the first 2000 iterations, with
pure exploitation, the exploitation baseline achieves a faster increase convergence.
But in the long run, all exr/exp algorithms improve the average CTR at convergence.
We have several observations regarding the different exr/exp algorithms. For the ε-
decreasing algorithm, the converged average CTR increases as the ε decreases (ex-
ploitation augments). For the ε-greedy(0.9) and ε-greedy(0.5), even after conver-
gence, the algorithms still give respectively 90% and 50% of the opportunities to
documents having low average CTR, which decreases significantly their results.
While the EG algorithm converges to a higher average CTR, its overall performance
is not as good as ε-decreasing. Its average CTR is low at the early step because of
more exploration, but does not converge faster. The contextual-ε-greedy algorithm
effectively learns the optimal ε; it has the best convergence rate, increases the average
CTR by a factor of 2 over the baseline and outperforms all other exr/exp algorithms.
The improvement comes from a dynamic tradeoff between exr/exp, controlled by the
critical situation (HLCS) estimation. At the early stage, this algorithm takes full ad-
vantage of exploration without wasting opportunities to establish good results.
8. 6 Conclusion
In this paper, we study the problem of exploitation and exploration in mobile context-
aware recommender systems and propose a novel approach that balances adaptively
exr/exp regarding the user’s situation. In order to evaluate the performance of the
proposed algorithm, we compare it with other standard exr/exp strategies. The exper-
imental results demonstrate that our algorithm performs better on average CTR in
various configurations. In the future, we plan to evaluate the scalability of the algo-
rithm on-board a mobile device and investigate other public benchmarks.
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