Yong Zheng. "Deviation-Based and Similarity-Based Contextual SLIM Recommendation Algorithms". ACM RecSys Doctoral Symposium, Proceedings of the 8th ACM Conference on Recommender Systems (ACM RecSys 2014), pp. 437-440, Silicon Valley, CA, USA, Oct 2014 [Doctoral Symposium, Acceptance rate: 47%]
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
Β
Context-aware recommender systems try to adapt to users' preferences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of the most popular contextual variables, but few researchers have explored how emotions take effect in recommendations -- especially the usage of the emotional variables other than the effectiveness alone. In this paper, we explore the role of emotions in context-aware recommendation algorithms. More specifically, we evaluate two types of popular context-aware recommendation algorithms -- context-aware splitting approaches and differential context modeling. We examine predictive performance, and also explore the usage of emotions to discover how emotional features interact with those context-aware recommendation algorithms in the recommendation process.
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...YONG ZHENG
Β
Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of context β identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.
[WI 2014]Context Recommendation Using Multi-label ClassificationYONG ZHENG
Β
Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set of items to a target user. In this paper, we consider the problem of recommending to a user the appropriate contexts in which an item should be selected. We believe that context recommenders can be used as another set of tools to assist users' decision making. We formulate the context recommendation problem and discuss the motivation behind and possible applications of the concept. We identify two general classes of algorithms to solve this problem: direct context prediction and indirect context recommendation. Furthermore, we present and evaluate several direct context prediction algorithms based on multi-label classification (MLC). Our experiments demonstrate that the proposed approaches outperform the baseline methods, and also that personalization is required to enhance the effectiveness of context recommenders.
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
Β
Context-aware recommender systems try to adapt to users' preferences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of the most popular contextual variables, but few researchers have explored how emotions take effect in recommendations -- especially the usage of the emotional variables other than the effectiveness alone. In this paper, we explore the role of emotions in context-aware recommendation algorithms. More specifically, we evaluate two types of popular context-aware recommendation algorithms -- context-aware splitting approaches and differential context modeling. We examine predictive performance, and also explore the usage of emotions to discover how emotional features interact with those context-aware recommendation algorithms in the recommendation process.
[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommend...YONG ZHENG
Β
Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of context β identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.
[WI 2014]Context Recommendation Using Multi-label ClassificationYONG ZHENG
Β
Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set of items to a target user. In this paper, we consider the problem of recommending to a user the appropriate contexts in which an item should be selected. We believe that context recommenders can be used as another set of tools to assist users' decision making. We formulate the context recommendation problem and discuss the motivation behind and possible applications of the concept. We identify two general classes of algorithms to solve this problem: direct context prediction and indirect context recommendation. Furthermore, we present and evaluate several direct context prediction algorithms based on multi-label classification (MLC). Our experiments demonstrate that the proposed approaches outperform the baseline methods, and also that personalization is required to enhance the effectiveness of context recommenders.
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Β
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to usersβ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem β differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Β
Context-aware recommender systems (CARS) adapt their recommendations to usersβ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach β differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
Summary of a Recommender Systems Survey paperChangsung Moon
Β
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, βRecommender Systems Survey,β Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
β’ Performed memory-based collaborative filtering techniques like Cosine similarities, Pearsonβs r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
β’ Studied the scalability of these methods on local machines & on Hadoop clusters
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
Β
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
Β
βRecommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an βActor-Criticβ reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
(Gaurav sawant & dhaval sawlani)bia 678 final project reportGaurav Sawant
Β
PROJECT REPORT
β’ Performed memory-based collaborative filtering techniques like Cosine similarities, Pearsonβs r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
β’ Studied the scalability of these methods on local machines & on Hadoop clusters
Techniques for Context-Aware and Cold-Start RecommendationsMatthias Braunhofer
Β
Context-aware recommender systems better identify interesting items for users by adapting their suggestions to the specific contextual situations, e.g., to the current weather, if an excursion is to be recommended . But, the cold-start problem may jeopardise the quality of the recommendations: for users, items or contextual situations that are new to the system, recommendations are hard to compute. We have developed a number of novel techniques to tame this problem, and in particular, new hybrid algorithms that combine several, simpler, algorithms in order to exploit their strengths and avoid their weaknesses. We have also developed algorithms for actively identifying the most useful preference information to ask the user in order to bootstrap the system. Our results obtained from a series of offline and online experiments reveal that the proposed techniques can effectively alleviate the cold-start problem of context-aware recommender systems.
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Β
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to usersβ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem β differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
[UMAP2013] Recommendation with Differential Context WeightingYONG ZHENG
Β
Context-aware recommender systems (CARS) adapt their recommendations to usersβ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called differential context relaxation (DCR), in which different subsets of contextual features were applied in different components of a recommendation algorithm. In this paper, we expand on our previous work on DCR, proposing a more general approach β differential context weighting (DCW), in which contextual features are weighted. We compare DCR and DCW on two real-world datasets, and DCW demonstrates improved accuracy over DCR with comparable coverage. We also show that particle swarm optimization (PSO) can be used to efficiently determine the weights for DCW.
Summary of a Recommender Systems Survey paperChangsung Moon
Β
This is the summary of the following paper:
J. Bobadilla, F. Ortega, A. Hernando and A. Gutierrez, βRecommender Systems Survey,β Knowledge Based Systems, Vol. 26, 2013, pp. 109-132.
β’ Performed memory-based collaborative filtering techniques like Cosine similarities, Pearsonβs r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
β’ Studied the scalability of these methods on local machines & on Hadoop clusters
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
Β
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
Β
βRecommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an βActor-Criticβ reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
(Gaurav sawant & dhaval sawlani)bia 678 final project reportGaurav Sawant
Β
PROJECT REPORT
β’ Performed memory-based collaborative filtering techniques like Cosine similarities, Pearsonβs r & model-based Matrix Factorization techniques like Alternating Least Squares (ALS) method
β’ Studied the scalability of these methods on local machines & on Hadoop clusters
Techniques for Context-Aware and Cold-Start RecommendationsMatthias Braunhofer
Β
Context-aware recommender systems better identify interesting items for users by adapting their suggestions to the specific contextual situations, e.g., to the current weather, if an excursion is to be recommended . But, the cold-start problem may jeopardise the quality of the recommendations: for users, items or contextual situations that are new to the system, recommendations are hard to compute. We have developed a number of novel techniques to tame this problem, and in particular, new hybrid algorithms that combine several, simpler, algorithms in order to exploit their strengths and avoid their weaknesses. We have also developed algorithms for actively identifying the most useful preference information to ask the user in order to bootstrap the system. Our results obtained from a series of offline and online experiments reveal that the proposed techniques can effectively alleviate the cold-start problem of context-aware recommender systems.
A novel deep-learning enabled, metric learning based recommendation model that significantly improves the state-of-the-art recommendation accuracy for a large number of recommendation tasks (news, books, photography, music, etc.)
Mining Large Streams of User Data for PersonalizedRecommenda.docxARIV4
Β
Mining Large Streams of User Data for Personalized
Recommendations
Xavier Amatriain
Netflix
[emailΒ protected]
ABSTRACT
The Netflix Prize put the spotlight on the use of data min-
ing and machine learning methods for predicting user pref-
erences. Many lessons came out of the competition. But
since then, Recommender Systems have evolved. This evo-
lution has been driven by the greater availability of different
kinds of user data in industry and the interest that the area
has drawn among the research community. The goal of this
paper is to give an up-to-date overview of the use of data
mining approaches for personalization and recommendation.
Using Netflix personalization as a motivating use case, I will
describe the use of different kinds of data and machine learn-
ing techniques.
After introducing the traditional approaches to recommen-
dation, I highlight some of the main lessons learned from
the Netflix Prize. I then describe the use of recommenda-
tion and personalization techniques at Netflix. Finally, I
pinpoint the most promising current research avenues and
unsolved problems that deserve attention in this domain.
1. INTRODUCTION
Recommender Systems (RS) are a prime example of the
mainstream applicability of large scale data mining. Ap-
plications such as e-commerce, search, Internet music and
video, gaming or even online dating make use of similar
techniques to mine large volumes of data to better match
their usersβ needs in a personalized fashion.
There is more to a good recommender system than the data
mining technique. Issues such as the user interaction design,
outside the scope of this paper, may have a deep impact
on the effectiveness of an approach. But given an existing
application, an improvement in the algorithm can have a
value of millions of dollars, and can even be the factor that
determines the success or failure of a business. On the other
hand, given an existing method or algorithm, adding more
features coming from different data sources can also result
in a significant improvement. I will describe the use of data,
models, and other personalization techniques at Netflix in
section 3. I will also discuss whether we should focus on
more data or better models in section 4.
Another important issue is how to measure the success of
a given personalization technique. Root mean squared er-
ror (RMSE) was the offline evaluation metric of choice in
the Netflix Prize (see Section 2). But there are many other
relevant metrics that, if optimized, would lead to different
solutions - think, for example, of ranking metrics such as
Normalized Discounted Cumulative Gain (NDCG) or other
information retrieval ones such as recall or area under the
curve (AUC). Beyond the optimization of a given offline met-
ric, what we are really pursuing is the impact of a method on
the business. Is there a way to relate the goodness of an algo-
rithm to more customer-facing metrics such as click-through
rate (CTR) or retention? I will describe our ...
A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given
domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility
which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the
constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to
model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very
hard to solve if it is poorly chosen
Presented on 4 Dec 2014
@Jeju, South Korea
Title: An efficient method for assessing the impact of refactoring candidates on maintainability based on matrix computation
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
By popular demand, here is a case study of my first Kaggle competition from about a year ago. Hope you find it useful. Thank you again to my fantastic team.
A database application differs form regular applications in that some of its inputs may be database queries. The program will execute the queries on a database and may use any result values in its subsequent program logic. This means that a user-supplied query may determine the values that the application will use in subsequent branching conditions. At the same time, a new database application is often required to work well on a body of existing data stored in some large database. For systematic testing of database applications, recent techniques replace the existing database with carefully crafted mock databases. Mock databases return values that will trigger as many execution paths in the application as possible and thereby maximize overall code coverage of the database application.
In this paper we offer an alternative approach to database application testing. Our goal is to support software engineers in focusing testing on the existing body of data the application is required to work well on. For that, we propose to side-step mock database generation and instead generate queries for the existing database. Our key insight is that we can use the information collected during previous program executions to systematically generate new queries that will maximize the coverage of the application under test, while guaranteeing that the generated test cases focus on the existing data.
[RIIT 2017] Identifying Grey Sheep Users By The Distribution of User Similari...YONG ZHENG
Β
Yong Zheng, Mayur Agnani, Mili Singh. βIdentifying Grey Sheep Users By The Distribution of User Similarities In Collaborative Filteringβ. Proceedings of The 6th ACM Conference on Research in Information Technology (RIIT), Rochester, NY, USA, October, 2017
A topic trend can be inferred by the usage of tags -- we name it as attention. Time-series analysis for tagging prediction can indicate the evolution of attention flow. This side takes political analysis for example, using time-series technique and discover interesting patterns.
[HetRec2011@RecSys]Experience Discovery: Hybrid Recommendation of Student Act...YONG ZHENG
Β
The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social networking site. Using pilot data, we are able to show that very simple aggregation techniques applied to the social network can improve recommendation accuracy.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
Β
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Β
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navyβs DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATOβs (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
Β
In this second installment of our Essentials of Automations webinar series, weβll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
Weβll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether youβre tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Letβs turn complexity into clarity and make your workspaces work wonders!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
Β
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
Β
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
Β
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Β
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Β
Monitoring and observability arenβt traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current companyβs observability stack.
While the dev and ops silo continues to crumbleβ¦.many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
π Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
Β
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Β
Clients donβt know what they donβt know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clientsβ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. Self-Introduction
β’ Yong Zheng, Ph.D. Candidate, DePaul University
β’ Research: Context-aware Collaborative Filtering
β’ Supervisor: Dr. Bamshad Mobasher
β’ Currently 5th Year at DePaul
β’ Expected Graduation: Summer, 2015
2Center for Web Intelligence, DePaul University, Chicago, USA
3. Outline
β’ Context-aware Recommender Systems (CARS)
β’ Context-aware Collaborative Filtering
β’ Contextual SLIM (CSLIM) Algorithms
β’ Current Work and Ongoing Work
β’ Challenges and Work in the Future
3Center for Web Intelligence, DePaul University, Chicago, USA
4. Outline
β’ Context-aware Recommender Systems (CARS)
β’ Context-aware Collaborative Filtering
β’ Contextual SLIM (CSLIM) Algorithms
β’ Current Work and Ongoing Work
β’ Challenges and Work in the Future
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5. Context-aware Recommender Systems (CARS)
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β’ What is Context?
βAny information that can be used to characterize the
situation of an entityβ, Abowd and Dey, 1999
Sample of popular contextual variables in Recommender Systems (RS):
Time (weekday/weekend/holiday), Location, Companion (kids/family),
Mood (happy/sad/excited/upset), Weather, etc
7. Context-aware Recommender Systems (CARS)
7Center for Web Intelligence, DePaul University, Chicago, USA
β’ Motivation Behind
Usersβ preferences change from contexts to contexts
β’ Basic Assumptions
RS should learn usersβ preferences in contexts c from
othersβ preferences in the same c, e.g. context-aware
collaborative filtering.
β’ Challenges
How to incorporate contexts into RS? E.g. CF, MF, etc
How to develop effective CARS?
How to Interpret contextual effects from the model?
Sparsity problems in CARS.
8. Outline
β’ Context-aware Recommender Systems (CARS)
β’ Context-aware Collaborative Filtering
β’ Contextual SLIM (CSLIM) Algorithms
β’ Current Work and Ongoing Work
β’ Challenges and Work in the Future
8Center for Web Intelligence, DePaul University, Chicago, USA
9. Context-aware Collaborative Filtering (CACF)
9Center for Web Intelligence, DePaul University, Chicago, USA
β’ Collaborative Filtering (CF)
CF is one of the most popular recommendation
algorithms in the traditional RS.
1). Memory-based CF, e.g., ItemKNN CF
2). Model-based CF, e.g., matrix factorization
3). Hybrid CF
β’ Context-aware Collaborative Filtering (CACF)
CF CACF
Contexts
10. Intro. Collaborative Filtering (CF)
10Center for Web Intelligence, DePaul University, Chicago, USA
There are three series of most popular CF algorithms
β’ Neighborhood-based CF
ItemKNN-based CF, Sarwar, et al, 2001
UserKNN-based CF, Resnick, et al, 1994
β’ Matrix factorization (MF)-based CF
Matrix Factorization, Sarwar, et al., 2000; Koren, et al., 2009
Tensor Factorization, Symeonidis, et al., 2008
β’ Sparse Linear Method (SLIM), Ning, et al., 2011
11. Intro. Collaborative Filtering (CF)
11Center for Web Intelligence, DePaul University, Chicago, USA
β’ Neighborhood-based CF
ItemKNN-based CF (ItemKNN) , Sarwar, et al, 2001
UserKNN-based CF (UserKNN) , Resnick, et al, 1994
M1 M2 M3 M4
U1 2 ? 5 4
U2 4 3 5 3
U3 4 3 1 4
12. Intro. Collaborative Filtering (CF)
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β’ ItemKNN, Sarwar, et al, 2001
ππ’,π =
πβπ π
π π’,π Γ π ππ(π, π
πβπ π
π ππ(π, π
Rating Prediction in ItemKNN:
Pros:
1). Straightforward
2). The explainability of the results
Cons:
1). Sparsity problem
2). Predictions rely on similarity (from co-ratings)
13. Intro. Collaborative Filtering (CF)
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β’ Matrix Factorization, Sarwar, et al., 2000; Koren, et al., 2009
Both users and items are represented by a vector of weights on
each Latent factors.
User vector ο¨ How users like some features
Item vector ο¨ How items obtain/capture those features
14. Intro. Collaborative Filtering (CF)
14Center for Web Intelligence, DePaul University, Chicago, USA
β’ Matrix Factorization, Sarwar, et al., 2000; Koren, et al., 2009
Pros:
1). Work effectively and efficiently (e.g.,MapReduce)
2). Being easy/flexible to incorporate side information
Cons:
1). Cold-start problem
2). Difficult to interpret the Latent factors
15. Intro. Collaborative Filtering (CF)
15Center for Web Intelligence, DePaul University, Chicago, USA
β’ Sparse Linear Model (SLIM), Ning, et al., 2011
ππ,π = π π,: β π:,π =
β=1,ββ π
π
π π,β πβ,πRanking Prediction in SLIM-I:
Matrix R = User-Item Rating matrix; W = Item-Item Coefficient matrix
16. Intro. Collaborative Filtering (CF)
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β’ Sparse Linear Model (SLIM), Ning, et al., 2011
ππ,π = π π,: β π:,π =
β=1,ββ π
π
π π,β πβ,π
Ranking Prediction in SLIM-I:
Results of Comparison between ItemKNN and SLIM-I:
1). Coefficients in W are similar to Item-item similarities in ItemKNN;
2). SLIM-I removed the normalization function;
3). The item-item coefficients DO NOT rely on co-ratings;
ππ’,π =
πβπ π
π π’,π Γ π ππ(π, π
πβπ π
π ππ(π, π
Rating Prediction in ItemKNN:
17. Intro. Collaborative Filtering (CF)
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β’ Sparse Linear Model (SLIM), Ning, et al., 2011
SLIM-I: Matrix W is an Item-Item Coefficient Matrix ItemKNN
SLIM-U: Matrix W is a User-User Coefficient Matrix UserKNN
Pros:
1).Avoid unreliable similarity calculations in ItemKNN/UserKNN
3.Work effectively and efficiently; Obtain explainability.
Cons: Cold-start problems
18. CF and Context-aware CF (CACF)
18Center for Web Intelligence, DePaul University, Chicago, USA
CF Neighborhood-based
Collaborative Filtering
Matrix
Factorization
SLIM
Pros Explainability Effectiveness
Efficience
Effectiveness
Explainability
Cons Sparsity Problem Weak
Explainability
Cold-start
Problem
CACF Differential Context
Modeling (DCM),2012
Tensor F, 2010
Context-aware
MF (CAMF),2011
CSLIM, 2014
Incorporate Contexts
19. CACF: Differential Context Modeling (DCM)
19Center for Web Intelligence, DePaul University, Chicago, USA
DCM incorporates contexts into neighborhood-based
CF (e.g., UserKNN, ItemKNN, Slope One) by applying
contextual constraints to different functional
components in the algorithms.
For example, the similarity of items can be calculated
within the same (or similar) contexts, instead of
calculations without considering contexts.
Drawbacks: Overfitting in Top-N Recommendations
20. CACF: Context-aware MF (CAMF)
20Center for Web Intelligence, DePaul University, Chicago, USA
CAMF incorporates contexts by adding contextual
rating deviations, which is actually a dependent
way to model the contextual effects.
bi is itemβs rating bias, which is replaced by an aggregation
of itemβs rating biases in different contextual situations.
Drawbacks: Difficult to interpret latent factors.
π π,π = π + π π’ + ππ + π π’
π
ππ
π π,π,{π1,π2,...,π π
= π + π π’ +
π=1
π
π΅πππ π
+ π π’
π
ππ
Rating Prediction in MF:
Rating Prediction in CAMF:
21. CACF: Tensor Factorization (TF)
21Center for Web Intelligence, DePaul University, Chicago, USA
TF is an independent way, which directly considers
each contextual variable as an individual context in
the multi-dimensional space.
Drawbacks:
1). Computational costs
increase exponentially
with the number of
contexts increases.
2). Contexts are usually
depdendent rather than
fully independent.
22. Outline
β’ Context-aware Recommender Systems (CARS)
β’ Context-aware Collaborative Filtering
β’ Contextual SLIM (CSLIM) Algorithms
β’ Current Work and Ongoing Work
β’ Challenges and Work in the Future
22Center for Web Intelligence, DePaul University, Chicago, USA
23. CACF: Contextual SLIM (CSLIM) Algorithms
23Center for Web Intelligence, DePaul University, Chicago, USA
CSLIM: Incorporate contexts into SLIM
ππ,π = π π,: β π:,π =
β=1,ββ π
π
π π,β πβ,πRanking Prediction in SLIM-I:
CSLIM has a uniform ranking prediction:
CSLIM aggregates contextual ratings with item-item coefficients.
However, there are two main points:
1).The rating to be aggregated should be placed under same c;
2).Accordingly, W indicates coefficients under same contexts;
Incorporate Contexts
ππ,π,π =
β=1,ββ π
π
π π,β,π πβ,π
24. CACF: Contextual SLIM (CSLIM) Algorithms
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Ranking prediction in CSLIM:
The remaining problem is how to calculate , it is
because context-aware data set is usually sparse β it is
not guaranteed that user also rated other items under
the same contexts c.
We use an estimation to obtain :
1).Deviation-Based CSLIM
2).Similarity-Based CSLIM
ππ,π,π =
β=1,ββ π
π
π π,β,π πβ,π
π π,β,π
π π,β,π
25. Outline
β’ Context-aware Recommender Systems (CARS)
β’ Context-aware Collaborative Filtering
β’ Contextual SLIM (CSLIM) Algorithms
β’ Current Work and Ongoing Work
β’ Challenges and Work in the Future
25Center for Web Intelligence, DePaul University, Chicago, USA
26. Deviation-Based CSLIM Algorithms
26Center for Web Intelligence, DePaul University, Chicago, USA
Research Status: Completed.
Publications:
β’ Y. Zheng, B. Mobasher, R. Burke. "CSLIM: Contextual
SLIM Recommendation Algorithms", ACM RecSys,
Silicon Valley, USA, 2014
β’ Y. Zheng. "Deviation-Based and Similarity-Based
Contextual SLIM Recommendation Algorithms", ACM
RecSys, Silicon Valley, USA, 2014
β’ Y. Zheng, B. Mobasher, R. Burke. "Deviation-Based
Contextual SLIM Recommenders", ACM CIKM,
Shanghai, China, 2014
27. Deviation-Based CSLIM Algorithms
27Center for Web Intelligence, DePaul University, Chicago, USA
Ranking prediction in CSLIM: ππ,π,π =
β=1,ββ π
π
π π,β,π πβ,π
π π,π,π = π π,π +
π=1
πΏ
π·π,π ππExample: CSLIM-I-CI,
R = non-contextual Rating Matrix
D = Contextual Rating Deviation Matrix
W = Item-item Coefficient Matrix
C = a binary context vector, as below
Weekend Weekday At Home At Park
1 0 0 1
28. Deviation-Based CSLIM Algorithms
28Center for Web Intelligence, DePaul University, Chicago, USA
Several CSLIM algorithms can be built:
1). Build upon ItemKNN or UserKNN
ItemKNN ο¨ CSLIM-I models, W is item-item matrix
UserKNN ο¨ CSLIM-U models, W is user-user matrix
2). How to define Contextual Rating Deviations?
Dependent with items ο¨ D is a Context-Item (CI) matrix
Dependent with users ο¨ D is a CU matrix
Individual devations ο¨ D is a vector of deviations
So, there are six models in total:
CSLIM-I-CI, CSLIM-I-CU, CSLIM-I-C;
CSLIM-U-CI, CSLIM-U-CU, CSLIM-U-C;
29. Deviation-Based CSLIM Algorithms (Optional)
29Center for Web Intelligence, DePaul University, Chicago, USA
Stay tuned:
Y. Zheng, B. Mobasher, R. Burke. "Deviation-Based
Contextual SLIM Recommenders", ACM CIKM, Shanghai,
China, 2014
New Updates:
1). Discover patterns how to select appropriate CSLIM
algorithms in advance based on data characteristics;
2). Develop general CSLIM algorithms, so that deviations
can be estimated from any contexts;
30. Deviation-Based CSLIM Algorithms
30Center for Web Intelligence, DePaul University, Chicago, USA
Deviation-Based CSLIM algorithms have been
demonstrated to outperform the state-of-the-art CACF
algorithms in terms of Top-N evaluation metrics, e.g.,
precision, recall, MAP, NDCG, etc.(over 5 data sets)
31. Similarity-Based CSLIM Algorithms
31Center for Web Intelligence, DePaul University, Chicago, USA
Research Status: Ongoing.
Ranking prediction in CSLIM:
, in this case, will be estimated by fusing with
similarity of two contexts.
Of course, there could be many more ways to model sim.
ππ,π,π =
β=1,ββ π
π
π π,β,π πβ,π
π π,β,π
π π’,π,πβ1 = π π’,π,πβ2 Γ πππ(πΎπππ , ππππ‘πππ Γ πππ(πππππππ¦, πππππππ
32. Outline
β’ Context-aware Recommender Systems (CARS)
β’ Context-aware Collaborative Filtering
β’ Contextual SLIM (CSLIM) Algorithms
β’ Current Work and Ongoing Work
β’ Challenges and Work in the Future
32Center for Web Intelligence, DePaul University, Chicago, USA
33. Challenges
33Center for Web Intelligence, DePaul University, Chicago, USA
1. Effectiveness
Several reviewers argued about scalability of CSLIM.
There are several solutions to reduce costs:
If Large # users/items ο¨ KNN, select Top-K neighbors;
If Large # contexts ο¨ select relevant ones only;
Besides, it is possible to use coordinate descent for
optimization instead of gradient descent.
2. Similarity of contexts
Most related work on the calculations of similarity
contexts rely on the existing contextual ratings.
34. Pros and Cons
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1. Pros
Effectiveness and also Explainability
2. Cons
Not all cells can be learned!
Sol: Factorize contexts/items to represent them by vectors
35. Future Work
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1. Explainability
Try to explore and discover the explainability of CSLIM
algorithms, e.g., discovering emotional effects compared
with other CARS algorithms (Zheng, et al, 2013, RecSys)
2. Scalability
Try to evaluate CSLIM on large data sets.
Notice: there are no real+large data in CARS domain!
3. Similarity-Based CSLIM models
Try to model and learn similarity of contexts in different
ways to develop similarity-based CSLIM models.
36. Acknowledgement
β’ NSF Student Funding
β’ ACM RecSys Doctoral Symposium
β’ Mentors: Dr. Pablo Castells and Dr. Irwin King
β’ My supervisor: Dr. Bamshad Mobasher
β’ My parents
36Center for Web Intelligence, DePaul University, Chicago, USA
37. Any Questions or Suggestions?
Yong Zheng, DePaul University, Chicago, USA
Oct 10, Doctoral Symposium @