The document describes a multi-criteria recommender system that exploits aspect-based sentiment analysis of user reviews. It involves a two-step methodology: 1) performing aspect extraction and sentiment analysis on user reviews using an algorithm based on SABRE to identify aspects, sub-aspects, and sentiment, and 2) creating and populating a multi-criteria data model with the extracted information and using it to generate recommendations. The system aims to develop a multi-criteria data model for recommendations without overwhelming users by automatically extracting product aspects and sentiments from reviews rather than requiring users to manually evaluate each aspect.
An overview of some deep learning methods for recommender systems along with an intro to the relevant deep learning methods such as convolutional neural networks (CNN's), recurrent neural networks (RNN's), autoencoders, restricted boltzmann machines (RBM's) and more.
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
A Learning to Rank Project on a Daily Song Ranking ProblemSease
Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situation; understanding how to adapt a specific dataset and to design the best approach to solve a ranking problem in a real-world scenario is thus crucial.This talk aims to illustrate how to set up and build a Learning to Rank (LTR) project starting from the available data, in our case a Spotify Dataset (available on Kaggle) on the Worldwide Daily Song Ranking, and ending with the implementation of a ranking model. A step by step (phased) approach to cope with this task using open source libraries will be presented.We will examine in depth the most important part of the pipeline that is the data preprocessing and in particular how to model and manipulate the features in order to create the proper input dataset, tailored to the machine learning algorithm requirements.
Time Series Analysis - 1 | Time Series in R | Time Series Forecasting | Data ...Simplilearn
This Time Series Analysis (Part-1) in R presentation will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data. A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this presentation and understand what is time series and how to implement time series using R.
Below topics are explained in this "Time Series in R Tutorial" -
1. Why time series?
2. What is time series?
3. Components of a time series
4. When not to use time series?
5. Why does a time series have to be stationary?
6. How to make a time series stationary?
7. Example: Forcast car sales for the 5th year
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/
An overview of some deep learning methods for recommender systems along with an intro to the relevant deep learning methods such as convolutional neural networks (CNN's), recurrent neural networks (RNN's), autoencoders, restricted boltzmann machines (RBM's) and more.
The slides from the Machine Learning Summers School 2015 in Sydney on Machine Learning for Recommender Systems. Collaborative filtering algorithms, Context-aware methods, Restricted Boltzmann Machines, Recurrent Neural Networks, Tensor Factorization, etc.
A Learning to Rank Project on a Daily Song Ranking ProblemSease
Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situation; understanding how to adapt a specific dataset and to design the best approach to solve a ranking problem in a real-world scenario is thus crucial.This talk aims to illustrate how to set up and build a Learning to Rank (LTR) project starting from the available data, in our case a Spotify Dataset (available on Kaggle) on the Worldwide Daily Song Ranking, and ending with the implementation of a ranking model. A step by step (phased) approach to cope with this task using open source libraries will be presented.We will examine in depth the most important part of the pipeline that is the data preprocessing and in particular how to model and manipulate the features in order to create the proper input dataset, tailored to the machine learning algorithm requirements.
Time Series Analysis - 1 | Time Series in R | Time Series Forecasting | Data ...Simplilearn
This Time Series Analysis (Part-1) in R presentation will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data. A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this presentation and understand what is time series and how to implement time series using R.
Below topics are explained in this "Time Series in R Tutorial" -
1. Why time series?
2. What is time series?
3. Components of a time series
4. When not to use time series?
5. Why does a time series have to be stationary?
6. How to make a time series stationary?
7. Example: Forcast car sales for the 5th year
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/
What if there’s a better way to run more complex tests and gain results faster? Joni explains the wonderful world of multi-armed bandit experiments.
About Joni
Joni Turunen is a Senior Developer at FROSMO currently working in the Product Team. He has vast experience with different frontend & backend technologies. With his 8 year history in the company he knows Frosmo's software inside out.
Learning to Rank (LTR) presentation at RELX Search Summit 2018. Contains information about history of LTR, taxonomy of LTR algorithms, popular algorithms, and case studies of applying LTR using the TMDB dataset using Solr, Elasticsearch and without index support.
Regression Analysis and model comparison on the Boston Housing DataShivaram Prakash
Creation of regression models to predict the median housing price using the Boston Housing dataset. Models used: Generalized linear model, generalized additive model, artificial neural networks, regression tree
Bundling - a new approach using Menu Based ConjointSKIM
Presented by:
Wessel Roose, Research Manager based in Rotterdam
As a service provider, for example if your brand is a financial institution or telecom company, you may try to increase your revenue by cross-selling additional services to your current customers.
One way to achieve this is offering multiple services in a “bundle” and making the bundle attractive to customers by offering it at a discount.
At our Research & Results workshop, we shared how to define bundles and optimize revenue by bundling. This is a new approach using our Menu Based Conjoint (MBC), our innovative method specifically designed for markets where the purchase choice is based on a menu.
Find out more at http://skimgroup.com/menu-based-choice-modeling.
IA générative : Menace ou Opportunité pour le SEOVincent Terrasi
La conférence présente un aperçu équilibré des opportunités et des menaces liées à l'IA générative dans le domaine du SEO. Elle met en évidence la nécessité d'une utilisation judicieuse de cette technologie pour maximiser ses avantages tout en atténuant ses inconvénients potentiels.
Netflix JavaScript Talks - Scaling A/B Testing on Netflix.com with Node.jsChris Saint-Amant
At Netflix we run hundreds of A/B tests every year. Maintaining multivariate experiences quickly adds strain to any UI engineering team. In this talk, Alex Liu and Micah Ransdell explore the patterns we’ve built in Node.js to tame this beast - ultimately enabling quick feature development and rapid test iteration on our service used by over 50 million people around the world.
Today, I had the big honor to give the opening keynote at the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020), being held virtually. HCOMP is the home of the human computation and crowdsourcing community working on frameworks, methods and systems that bring together people and machine intelligence to achieve better results. I decided to totally revamp a previous talk to focus on so-called "human in the loop" and showed how we incorporate human in the loop to personalise at scale, with some of the research at Spotify. Sharing the slides for general interests.
At the BCS Search Solutions 2018, I gave a talk about work on search we are doing at Spotify. The talk described what search means in the context of Spotify, how it differs what we know about search, and the challenges associated with understanding user intents and mindsets in an "entertainment" context. The talk also discussed various efforts at Spotify to understand why users submit search queries, what they expect, how they assess their search experience, and how Spotify responds to these search queries. This is work done with many colleagues at Spotify in Boston, London, New York and Stockholm, and our wonderful summer interns.
When marketing teams spend money on a paid acquisitions program it is crucial to understand the effect of that ad spend. In this talk, we will outline incrementality as a way to measure the causal impact that ad spend has on acquiring new customers and its advantages over more traditional metrics. We will walk through several ad measurement products available today and give examples of how to apply them to your business.
Recurrent Neural Networks for Recommendations and Personalization with Nick P...Databricks
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results.
This talk explores the latest research advances in this domain, as well as practical applications. I will provide an overview of RNNs, covering common architectures and applications, before diving deeper into RNNs for session-based recommendations. I will pay particular attention to the challenges inherent in common personalization tasks and the specific adjustments to models and optimization techniques required for success.
Empathic inclination from digital footprints
Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, Marco de Gemmis and Giovanni Semeraro
University of Bari “Aldo Moro”, Dept. of Computer Science, Italy
[MMIR@MM2023] On Popularity Bias of Multimodal-aware Recommender Systems: A M...Daniele Malitesta
Slides for the paper "On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis", accepted and presented at the 1st International Workshop on Deep Multimodal Learning for Information Retrieval, co-located with the 31st ACM International Conference on Multimedia (MMIR@MM'23).
Paper: https://dl.acm.org/doi/abs/10.1145/3606040.3617441
Code: https://github.com/sisinflab/MultiMod-Popularity-Bias
What if there’s a better way to run more complex tests and gain results faster? Joni explains the wonderful world of multi-armed bandit experiments.
About Joni
Joni Turunen is a Senior Developer at FROSMO currently working in the Product Team. He has vast experience with different frontend & backend technologies. With his 8 year history in the company he knows Frosmo's software inside out.
Learning to Rank (LTR) presentation at RELX Search Summit 2018. Contains information about history of LTR, taxonomy of LTR algorithms, popular algorithms, and case studies of applying LTR using the TMDB dataset using Solr, Elasticsearch and without index support.
Regression Analysis and model comparison on the Boston Housing DataShivaram Prakash
Creation of regression models to predict the median housing price using the Boston Housing dataset. Models used: Generalized linear model, generalized additive model, artificial neural networks, regression tree
Bundling - a new approach using Menu Based ConjointSKIM
Presented by:
Wessel Roose, Research Manager based in Rotterdam
As a service provider, for example if your brand is a financial institution or telecom company, you may try to increase your revenue by cross-selling additional services to your current customers.
One way to achieve this is offering multiple services in a “bundle” and making the bundle attractive to customers by offering it at a discount.
At our Research & Results workshop, we shared how to define bundles and optimize revenue by bundling. This is a new approach using our Menu Based Conjoint (MBC), our innovative method specifically designed for markets where the purchase choice is based on a menu.
Find out more at http://skimgroup.com/menu-based-choice-modeling.
IA générative : Menace ou Opportunité pour le SEOVincent Terrasi
La conférence présente un aperçu équilibré des opportunités et des menaces liées à l'IA générative dans le domaine du SEO. Elle met en évidence la nécessité d'une utilisation judicieuse de cette technologie pour maximiser ses avantages tout en atténuant ses inconvénients potentiels.
Netflix JavaScript Talks - Scaling A/B Testing on Netflix.com with Node.jsChris Saint-Amant
At Netflix we run hundreds of A/B tests every year. Maintaining multivariate experiences quickly adds strain to any UI engineering team. In this talk, Alex Liu and Micah Ransdell explore the patterns we’ve built in Node.js to tame this beast - ultimately enabling quick feature development and rapid test iteration on our service used by over 50 million people around the world.
Today, I had the big honor to give the opening keynote at the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020), being held virtually. HCOMP is the home of the human computation and crowdsourcing community working on frameworks, methods and systems that bring together people and machine intelligence to achieve better results. I decided to totally revamp a previous talk to focus on so-called "human in the loop" and showed how we incorporate human in the loop to personalise at scale, with some of the research at Spotify. Sharing the slides for general interests.
At the BCS Search Solutions 2018, I gave a talk about work on search we are doing at Spotify. The talk described what search means in the context of Spotify, how it differs what we know about search, and the challenges associated with understanding user intents and mindsets in an "entertainment" context. The talk also discussed various efforts at Spotify to understand why users submit search queries, what they expect, how they assess their search experience, and how Spotify responds to these search queries. This is work done with many colleagues at Spotify in Boston, London, New York and Stockholm, and our wonderful summer interns.
When marketing teams spend money on a paid acquisitions program it is crucial to understand the effect of that ad spend. In this talk, we will outline incrementality as a way to measure the causal impact that ad spend has on acquiring new customers and its advantages over more traditional metrics. We will walk through several ad measurement products available today and give examples of how to apply them to your business.
Recurrent Neural Networks for Recommendations and Personalization with Nick P...Databricks
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results.
This talk explores the latest research advances in this domain, as well as practical applications. I will provide an overview of RNNs, covering common architectures and applications, before diving deeper into RNNs for session-based recommendations. I will pay particular attention to the challenges inherent in common personalization tasks and the specific adjustments to models and optimization techniques required for success.
Empathic inclination from digital footprints
Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, Marco de Gemmis and Giovanni Semeraro
University of Bari “Aldo Moro”, Dept. of Computer Science, Italy
[MMIR@MM2023] On Popularity Bias of Multimodal-aware Recommender Systems: A M...Daniele Malitesta
Slides for the paper "On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis", accepted and presented at the 1st International Workshop on Deep Multimodal Learning for Information Retrieval, co-located with the 31st ACM International Conference on Multimedia (MMIR@MM'23).
Paper: https://dl.acm.org/doi/abs/10.1145/3606040.3617441
Code: https://github.com/sisinflab/MultiMod-Popularity-Bias
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
QuESo: a Quality Model for Open Source Software EcosystemsGESSI UPC
Open source software has witnessed an exponential growth in the last two decades and it is playing an increasingly
important role in many companies and organizations leading to the formation of open source software
ecosystems. In this paper we present a quality model that will allow the evaluation of those ecosystems in
terms of their relevant quality characteristics such as health or activeness.
Analysis, design and implementation of a Multi-Criteria Recommender System ba...Davide Giannico
"Analysis, design and implementation of a Multi-Criteria Recommender System based on Aspect Extraction and Sentiment Analysis techniques" is my final work presentation for the Master of Computer Science at University of Bari "Aldo Moro"(Italy).
This work mainly discusses two algorithms of multi-criteria recommendation based on the extracted information from the item reviews using Aspect Extraction and Sentiment Analysis techniques.
The user usually doesn't read all the reviews correlated to each item, ignoring a lot of useful information. This happens because that analysis takes a lot of time and energy.
The main opportunity for us has been to take advantage of the reviews informative power, incorporating such information in the recommendation process.
Moreover the existing systems usually use a default taxonomies of criteria on which the user can express his rate.The most of the times these criteria are not exhaustive respect to user preferences and vague (not specific). Differently our approach is based on these three steps: Automatic identification of criteria from the reviews using Aspect Extraction techniques, Sentiment Analysis for associating a preference level to the extracted criteria (implicit rating) and Extension of multi-criteria item-based recommendation algorithms exploiting the information extracted.
Both the algorithms introduces the multi-criteria component in the item-to-item matrix calculus. The Aspect extraction and Sentiment Analysis step has been possible using an other system named ORE (Opinion Original Engine) who belongs to the Department of Computer Science (University of Bari "Aldo Moro").
The first algorithm named #Multi-ORE-criteria uses the multi-ORE-ratings for calculating the similarity item-to-item matrix using several mutli-criteria similarity metrics (Pearson, Euclidean e.g.)
The second algorithm named #MARTA (Multi-criteria Aspect-based Recommender system based on sentimenT Analysis ) uses each item description for calculating the similarity item-to-item matrix using several mutli-criteria similarity metrics (Pearson, Euclidean e.g.). The item description format consists of several (A,S) pairs where A is the extracted aspect and S is its related score. This step has been possible using Aspect Extraction and Sentiment Analysis techniques on the reviews dataset.
The implementation of both is based on Mahout Machine Learning library (http://mahout.apache.org/), extending the Item-Based Recommendation algorithms.
In the last part we discuss about the experimentation, results, conclusions and futur work.
A Review on Sentimental Analysis of Application ReviewsIJMER
As with rapid evolution of computer technology and smart phones mobile applications
become very important part of our life. It is very difficult for customers to keep track of different
applications reviews so sentimental analysis is used. Sentimental analysis is effective and efficient
evolution of customer’s opinion in real time. Sentimental analysis for applications review is performed
two approaches statistical model based approaches and Natural Language Processing (NLP) based
approaches to create rules. Two schemes used for analyzing the textual comments- aspect level
sentimental analysis analyses the text and provide a label on each aspect then scores on multiple
aspects are aggregated and result for reviews shown in graphs. Second scheme is document level
analyses which comprising of adjectives, adverbs and verbs and n-gram feature extraction. I have also
used our SentiWordNet scheme to compute the document-level sentiment for each movie reviewed
and compared the results with results obtained using Alchemy API. The sentiment profile of a movie is
also compared with the document-level sentiment result. The results obtained show that my scheme
produces a more accurate and focused sentiment profile than the simple document-level sentiment
analysis.
httpowl.english.purdue.eduowlresource54401 The PurPazSilviapm
http://owl.english.purdue.edu/owl/resource/544/01/
The Purdue OWL: Sample Outlines
Alphanumeric Outline
THE COLLEGE APPLICATION PROCESS
I. CHOOSE DESIRED COLLEGES
A. Visit and evaluate college campuses
B. Visit and evaluate college websites
1. Look for interesting classes
2. Note important statistics
II. PREPARE APPLICATION
A. Write personal statement
1. Choose interesting topic
a. Describe an influential person in your life
(1) Favorite high school teacher
(2) Grandparent
b. Describe a challenging life event
2. Include important personal details
a. Volunteer work
b. Participation in varsity sports
B. Revise personal statement
III. COMPILE RÉSUMÉ
A. List relevant coursework
B. List work experience
C. List volunteer experience
1. Tutor at foreign language summer camp
2. Counselor for suicide prevention hotline
http://owl.english.purdue.edu/owl/resource/544/01/
Full Sentence Outline
I. Man-made pollution is the primary cause of global warming.
A. Greenhouse gas emissions are widely identified by the scientific community to be
harmful.
1. The burning of coal and fossil fuels are the primary releasers of hazardous
greenhouse gases.
Full sentence outlines are often accompanied with an APA reference list on a separate
page. Quotes within the outline must also utilize APA in-text citations.
Decimal Outline
1.0 Choose Desired College
1.1 Visit and evaluate college campuses
1.2 Visit and evaluate college websites
1.2.1 Look for interesting classes
1.2.2 Note important statistics
Designing Business Intelligence System with PageRank Algorithm 5
Designing Business Intelligence System with PageRank Algorithm
Dinesh Kalla
CS 857 : Business Intelligence : IP 2
Colorado Technical University
Dr D Revenaugh
17th March 2021
Problem Statement
Organizations are continuously bombarded with vast amounts of data. Managers must gain the expertise needed to fully utilize the benefits that come with business intelligence systems (Olszak, 2016). Data mining has become an essential tool for managers that provide insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships (Visinescu, 2017). Additionally, managers require business intelligence systems that can rank the output in the order of priority. PageRank algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review (Florescu, 2017). The purpose of this research paper is to demonstrate how the PageRank algorithm can be implemented in business intelligence (BI) systems to support managers in decision-making in hiring active authors or researchers belongs to computer science field of study by displaying query results in order of their significance in supporting hiring processes (Kanakia, 2019).
Justification / Literature Review
Algorithms ar ...
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on microblogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’schef. This paper explain different methods of text preprocessing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
Music Recommendation System with User-based and Item-based Collaborative Filt...ijeei-iaes
Internet and E-commerce are the generators of abundant of data, causing information Overloading. The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method.
A Framework for Holistic User Modeling Merging Heterogeneous Digital FootprintsCataldo Musto
A Framework for Holistic User Modeling Merging Heterogeneous Digital Footprints - HUM 2018 – Holistic User Modeling Workshop jointly held with
UMAP 2018 – 26th International
Conference on User Modeling,
Adaptation and Personalization
Singapore - July 8, 2018
Modeling For Sustainability: Or How to Make Smart CPS Smarter?Benoit Combemale
Various disciplines use models for different purposes. An engineering model, including a software engineering model, is often developed to guide the construction of a non-existent system. A scientific model is created to better understand an existing phenomenon (i.e., an already existing system or a physical phenomenon). An engineering model may incorporate scientific models to build a smart cyber-physical system (CPS) that require an understanding of the surrounding environment to decide of the relevant adaptation to apply. Sustainability systems, i.e., smart CPS managing resource production, transport and consumption for the sake of sustainability (e.g., smart grid, city, farming system…), are typical examples of smart CPS. Due to the inherent complex nature of sustainability that must delicately balance trade-offs between social, environmental, and economic concerns, modeling challenges abound for both the scientific and engineering disciplines.
In this talk, I will present a vision that promotes a unique approach combining engineering and scientific models to enable informed decision on the basis of open and scientific knowledge, a broader engagement of society for addressing sustainability concerns, and incorporate those decisions in the control loop of smart CPS. I will introduce a research roadmap to support this vision that emphasizes the socio-technical benefits of modeling.
Similar to A Multi-Criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews (20)
Intelligenza Artificiale e Social Media - Monitoraggio della Farnesina e La M...Cataldo Musto
Convegno a Porte Chiuse dell'Associazione Italiana per l'Intelligenza Artificiale insieme al Ministero per gli Affari Esteri e la Cooperazione Internazionale - 30 Giugno 2021
Exploring the Effects of Natural Language Justifications in Food Recommender ...Cataldo Musto
Cataldo Musto, Alain D. Starke, Christoph Trattner, Amon Rapp, and Giovanni Semeraro. 2021. Exploring the Effects of Natural Language Justifications in Food Recommender Systems. In Proceedings of the 29th ACM
Conference on User Modeling, Adaptation and Personalization (UMAP ’21), June 21–25, 2021, Utrecht, Netherlands. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3450613.3456827
Natural Language Justifications for Recommender Systems Exploiting Text Summa...Cataldo Musto
Natural Language Justifications for Recommender Systems Exploiting Text Summarization and Sentiment Analysis - AI*IA 2019 - Italian Conference on Artificial Intelligence
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
A Multi-Criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews
1. @cataldomusto @pasqualelops
@semeraro_g @SWAP_research
A Multi-criteria Recommender System
Exploiting Aspect-based Sentiment
Analysis of Users’ Reviews
CATALDO MUSTO, MARCO DE GEMMIS, GIOVANNI SEMERARO, PASQUALE LOPS
UNIVERSITÀ DEGLI STUDI DI BARI ‘ALDO MORO’ - ITALY
RecSys 2017 - 11th ACM Conference on
Recommender Systems
Como, Italy
August 30, 2017
cataldo.musto@uniba.it
2. Multi-Criteria RecSys
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Not a new concept (*)
Each user evaluates
each aspect of
the item
(*) Adomavicius, Gediminas, and YoungOk Kwon. "Multi-criteria recommender
systems." Recommender Systems Handbook. Springer US, 2015. 847-880.
3. Multi-Criteria RecSys
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Not a new concept (*)
Each user evaluates
each aspect of
the item
(*) Adomavicius, Gediminas, and YoungOk Kwon. "Multi-criteria recommender
systems." Recommender Systems Handbook. Springer US, 2015. 847-880.
Problem:
Overwhelming!
4. Multi-Criteria RecSys
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Not a new concept (*)
Each user evaluates
each aspect of
the item
(*) Adomavicius, Gediminas, and YoungOk Kwon. "Multi-criteria recommender
systems." Recommender Systems Handbook. Springer US, 2015. 847-880.
Problem: Aspects
are not fixed!
5. Multi-Criteria RecSys
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Not a new concept (*)
Each user evaluates
each aspect of
the item
(*) Adomavicius, Gediminas, and YoungOk Kwon. "Multi-criteria recommender
systems." Recommender Systems Handbook. Springer US, 2015. 847-880.
Problem: Aspects can
be further modeled as a
hierarchy
6. Research Question
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
How to develop a
multi-criteria data model
without overwhelming
the user ?
7. Research Question
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
What is the performance of
such a data model in a
collaborative
recommendation scenario?
8. Our contribution
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
A multi-criteria collaborative
recommendation methodology exploiting
aspect-based sentiment analysis of users’ reviews
9. Methodology
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Input: textual reviews
Step 1: aspect extraction and sentiment analysis
Step 2: creating multi-criteria data model
Output: recommendations
10. Methodology
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Input: textual reviews
Step 1: aspect extraction and sentiment analysis
Step 2: creating multi-criteria data model
Output: recommendations
11. Methodology
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Input: textual reviews
Step 1: aspect extraction and sentiment analysis
Step 2: creating and filling our multi-criteria data model
Output: recommendations
12. Methodology
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Input: textual reviews
Step 1: aspect extraction and sentiment analysis
Step 2: creating and filling our multi-criteria data model
Output: recommendations
13. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Algorithm based on SABRE(*)
(Sentiment Aspect-based Retrieval Engine)
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
𝑅 = {𝑟1, 𝑟2 … 𝑟 𝑛}Input: set of reviews
Output: quintuples < 𝑟𝑖, 𝑎𝑖𝑗, 𝑎𝑖𝑗𝑘, 𝑟𝑒𝑙 𝑎𝑖𝑗𝑘, 𝑟𝑖 , 𝑠𝑒𝑛𝑡(𝑎𝑖𝑗𝑘, 𝑟𝑖) >
14. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Algorithm based on SABRE(*)
(Sentiment Aspect-based Retrieval Engine)
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
𝑅 = {𝑟1, 𝑟2 … 𝑟 𝑛}Input: set of reviews
Output: quintuples < 𝑟𝑖, 𝑎𝑖𝑗, 𝑎𝑖𝑗𝑘, 𝑟𝑒𝑙 𝑎𝑖𝑗𝑘, 𝑟𝑖 , 𝑠𝑒𝑛𝑡(𝑎𝑖𝑗𝑘, 𝑟𝑖) >
𝑟𝑖 =
𝑎𝑖𝑗, 𝑎𝑖𝑗𝑘=
𝑟𝑒𝑙 𝑎𝑖𝑗𝑘, 𝑟𝑖 , 𝑠𝑒𝑛𝑡(𝑎𝑖𝑗𝑘, 𝑟𝑖) =
i-th review
j-th aspect and k-th sub-aspect in the i-th review
relevance and sentiment
15. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Algorithm based on SABRE(*)
(Sentiment Aspect-based Retrieval Engine)
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
𝑅 = {𝑟1, 𝑟2 … 𝑟 𝑛}Input: set of reviews
Output: quintuples < 𝑟𝑖, 𝑎𝑖𝑗, 𝑎𝑖𝑗𝑘, 𝑟𝑒𝑙 𝑎𝑖𝑗𝑘, 𝑟𝑖 , 𝑠𝑒𝑛𝑡(𝑎𝑖𝑗𝑘, 𝑟𝑖) >
𝑟𝑖 =
𝑎𝑖𝑗, 𝑎𝑖𝑗𝑘=
𝑟𝑒𝑙 𝑎𝑖𝑗𝑘, 𝑟𝑖 , 𝑠𝑒𝑛𝑡(𝑎𝑖𝑗𝑘, 𝑟𝑖) =
i-th review
j-th aspect and k-th sub-aspect in the i-th review
relevance and sentiment
How do we extract aspects, relevance and sentiment?
16. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Statistical approach based on the Kullback-Leibler (KL) Divergence
17. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Statistical approach based on the Kullback-Leibler (KL) Divergence
Measures the difference between the distribution of a term
in a generic corpus (e.g. BNC) and its distribution in a domain corpus
(e.g. hotel reviews)
Insight: the higher the divergence, the higher the
importance of the term in the domain
18. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Statistical approach based on the Kullback-Leibler (KL) Divergence
Measures the difference between the distribution of a term
in a generic corpus (e.g. BNC) and its distribution in a domain corpus
(e.g. hotel reviews)
Insight: the higher the divergence, the higher the
importance of the term in the domain
KL(room, BNC, hotel-reviews) >> 0
KL(food, BNC, hotel-reviews) > 0
KL(place, BNC, hotel-reviews) ~ 0
KL(politics, BNC, hotel-reviews) ~ 0
We label as ‘aspects’ the
nouns whose
KL-divergence is higher
than zero
19. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Statistical approach based on the Kullback-Leibler (KL) Divergence
Measures the difference between the distribution of a term
in a generic corpus (e.g. BNC) and its distribution in a domain corpus
(e.g. hotel reviews)
Insight: the higher the divergence, the higher the
importance of the term in the domain
KL(room, BNC, hotel-reviews) >> 0 YES
KL(food, BNC, hotel-reviews) > 0 YES
KL(place, BNC, hotel-reviews) ~ 0 NO
KL(politics, BNC, hotel-reviews) ~ 0 NO
20. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Statistical approach based on the Kullback-Leibler (KL) Divergence
Measures the difference between the distribution of a term
in a generic corpus (e.g. BNC) and its distribution in a domain corpus
(e.g. hotel reviews)
Insight: the higher the divergence, the higher the
importance of the term in the domain
KL(room, BNC, hotel-reviews) >> 0 YES
KL(food, BNC, hotel-reviews) > 0 YES
KL(place, BNC, hotel-reviews) ~ 0 NO
KL(politics, BNC, hotel-reviews) ~ 0 NO
Distinguishing aspect:
the set is not fixed!
21. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Sub-aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Another distinguishing aspect: we can extract a hierarchy of terms
Based on Phraseness and Informativeness: They measure the gain
in information if two terms are modeled together
Insight: if phraseness and informativeness are high,
the terms have an high cohesion
22. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Sub-aspect Extraction
(*) Caputo, A., Basile, P., de Gemmis, M., Lops, P., Semeraro, G., & Rossiello, G. (2017). SABRE: A
Sentiment Aspect-Based Retrieval Engine. In Information Filtering and Retrieval (pp. 63-78).
Another distinguishing aspect: we can extract a hierarchy of terms
Based on Phraseness and Informativeness: They measure the gain
in information if two terms are modeled together
Insight: if phraseness and informativeness are high,
the terms have an high cohesion
SUB(room, food, hotel-reviews) ~ 0 NO
SUB(room, shower, hotel-reviews) > 0 YES
SUB(food, wine, hotel-reviews) > 0 YES
SUB(food, service, hotel-reviews) ~ 0 NO
23. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Sabre@Work
(*) Real review of the hotel
we actually stay in Como :)
(*)
24. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
< 𝑟, 𝑏𝑟𝑒𝑎𝑘𝑓𝑎𝑠𝑡,∗, 1.5, 0.5 >
Sabre@Work
(*) Real review of the hotel
we actually stay in Como :)
(*)
25. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
< 𝑟, 𝑏𝑟𝑒𝑎𝑘𝑓𝑎𝑠𝑡,∗, 1.5, 0.5 >
No sub-aspects Relevance=KL-divergence score
Sentiment = lexicon-based approach based on AFINN
wordlist (*) or machine-learning based approach based on
CoreNLP (^)
(*) http://neuro.imm.dtu.dk/wiki/AFINN
(^) https://nlp.stanford.edu/sentiment/code.html
Sabre@Work
(*) Real review of the hotel
we actually stay in Como :)
(*)
26. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
< 𝑟, 𝑏𝑟𝑒𝑎𝑘𝑓𝑎𝑠𝑡,∗, 1.5, 0.5 >
< 𝑟, 𝑟𝑜𝑜𝑚, 𝑠ℎ𝑜𝑤𝑒𝑟, 1.2, −0.5 >
Sabre@Work
(*) Real review of the hotel
we actually stay in Como :)
(*)
27. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
< 𝑟, 𝑏𝑟𝑒𝑎𝑘𝑓𝑎𝑠𝑡,∗, 1.5, 0.5 >
< 𝑟, 𝑟𝑜𝑜𝑚, 𝑠ℎ𝑜𝑤𝑒𝑟, 1.2, −0.5 >
< 𝑟, 𝑟𝑜𝑜𝑚,∗, 1.3, 0.2 >
Sabre@Work
(*) Real review of the hotel
we actually stay in Como :)
(*)
28. Aspect Extraction and Sentiment Analysis
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
< 𝑟, 𝑏𝑟𝑒𝑎𝑘𝑓𝑎𝑠𝑡,∗, 1.5, 0.5 >
< 𝑟, 𝑟𝑜𝑜𝑚, 𝑠ℎ𝑜𝑤𝑒𝑟, 1.2, −0.5 >
< 𝑟, 𝑟𝑜𝑜𝑚,∗, 1.3, 0.2 >
Sabre@Work
… … . 𝑒𝑡𝑐.
(*) Real review of the hotel
we actually stay in Como :)
(*)
29. Multi-Criteria Data Model
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
A multi-criteria
data model is
automatically
filled by
exploiting the
aspects
extracted from
the review and
their sentiment
Finer-Grained
Representation!
30. Providing Recommendations
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
Similarity is
calculated through
multi-criteria
Euclidean distance
Recommendations
are provided by
exploiting both
User-to-User and
Item-to-Item
Collaborative
Filtering
Recommendation
31. Framework Recap
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
32. Experiments
Which combination of the
parameters led to the best
predictive accuracy?
How does our framework perform
when compared to single-criteria
recommendations and matrix
factorization tecniques?
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
33. Datasets
Yelp
45,981 users
11,537 items
229,606 ratings(*)
99.95% sparsity
TripAdvisor
536,952 users
3,945 items
796,958 ratings(*)
99.96% sparsity
Amazon
826,773 users
50,210 items
1,324,759 ratings(*)
99.99% sparsity
(*) Ratings = ratings + reviews
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
34. Datasets
Yelp
45,981 users
11,537 items
229,606 ratings(*)
99.95% sparsity
TripAdvisor
536,952 users
3,945 items
796,958 ratings(*)
99.96% sparsity
Amazon
826,773 users
50,210 items
1,324,759 ratings(*)
99.99% sparsity
(*) Ratings = ratings + reviews
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
35. Experimental Settings
Review Processing
◦ Stop-Word removed
◦ Entity and Collocations recognized
SABRE parameters
◦ With/without subaspects
◦ #aspects/#subaspects = 10, 50
◦ KL-divergence threshold = 0.1
◦ Only nouns!
Recommendations
◦ Multi-Criteria U2U and I2I
Metric
◦ MAE (calculated with Rival framework)
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
36. Baselines
Single-Criteria Recommendations
techniques
◦ User-to-User Collaborative Filtering
◦ Item-to-Item Collaborative Filtering
Static Multi-Criteria Recommendations
◦ Only on TripAdvisor data
Matrix Factorization techniques
◦ SGD (Stochastic Gradient Descent)
◦ ParallelSGD
◦ ALSWR
◦ Implementations available in Mahout
◦ Tuning of parameters
Alessandro Suglia, Claudio Greco, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro.
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks. UMAP 2017. Bratislava, Slovakia. July 12, 2017
38. 0,7111
0,7564
0,7269
0,8007
0,65
0,67
0,69
0,71
0,73
0,75
0,77
0,79
0,81
0,83
TripAdvisor
10 neigh. / 10 aspects / sub-aspects 10 neigh. / 10 aspects / no sub-aspects
10 neigh. / 50 aspects / sub-aspects 10 neigh. / 50 aspects / no sub-aspects
Outcomes
Best-results obtained
with 10 aspects
Best-results obtained
by also introducing
sub-aspects
(Amazon had a
different behavior)
Lower MAE!
Results – Multi-Criteria User-to-User CF
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
39. 0,7111
0,798
0,8245
0,8429
0,6
0,65
0,7
0,75
0,8
0,85
TripAdvisor
Multi-U2U Static-Multi-U2U Multi-I2I Static-Multi-I2I
Outcomes
TripAdvisors data
included ratings
about six static
aspects (cleanliness,
location, value,
service, sleep quality,
overall)
Our approach based
on unsupervised
aspect extraction also
improved these
results
Results – vs. Static Multi-Criteria RecSys
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
40. 0,7111
0,8337
0,745 0,7449
0,9053
0,65
0,7
0,75
0,8
0,85
0,9
0,95
1
TripAdvisor
Multi-U2U Single-U2U Ratings-SGD Parallel-SGD ALSWR
Outcomes
Our approach
overcomes all the
baselines.
Our framework wins
the comparisons to
Single-U2U and
Single-I2I
Also matrix
factorization
techniques got an
higher MAE
Results – Baselines
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017
41. Recap
Results
☺ Our framework significantly improves all the baselines
☺ Unsupervised Aspect Extraction also overcomes static aspects
Future Work: evaluate data model with more sophisticated algorithms
Cataldo Musto, Marco de Gemmis, Giovanni Semeraro. Pasquale Lops
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews. RECSYS 2017. Como, Italy. August 30, 2017