Integrating selection-based aspect sentiment and preference knowledge for social recommender systems.

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach: The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings: Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications: The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value: This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches

Scalable data analytics using spark

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.This thesis presents our experience in designing a scalable data analytics platform on top of Apache Spark (major) and Apache Hadoop (minor). We worked on three repre- sentative applications: (1) Sentiment Analysis, (2) Collaborative Filtering and (3) Topic Modeling. We demonstrated how to scale these applications on a cluster of 8 workers. Each worker contributes 4 cores, 8 GB RAM, and 100 GB of disk space to the com- pute pool. Our conclusion is that Apache Spark has enough maturity to be deployed in production comfortably.Abstract ii Öz iii Acknowledgments v List of Figures viii List of Tables ix 1 Introduction 1 2 Sentiment Analytics on Spark 2 2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2.1 Preprocessing on the data . . . . . . . . . . . . . . . . . . . . . . . 3 2.2.2 Naive Bayes Classifier . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3.1 Resilient Distributed Datasets(RDD) . . . . . . . . . . . . . . . . . 5 2.3.2 Broadcast Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.3 The Movie Reviews Dataset . . . . . . . . . . . . . . . . . . . . . . 6 2.3.4 Cluster Configuration . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3.5 Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.1 Apache Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.2 Apache Mahout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4.3 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.3.1 Broadcasting vs. Not-broadcasting . . . . . . . . . . . . . 10 2.4.3.2 Time required for training . . . . . . . . . . . . . . . . . . 10 2.4.3.3 Time required for testing . . . . . . . . . . . . . . . . . . 11 3 Collaborative Filtering on Spark 13 3.1 MLBase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Online Recommendation System . . . . . . . . . . . . . . . . . . . . . . . 14 4 Topic Modeling on Hadoop 17 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4.3 LDA in MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.4.1 The Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.4.2 Cluster Configuration . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.4.3 Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5 Conclusions 22 Bibliography 2

A fuzzy-based approach for classifying students' emotional states in online collaborative work

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.Peer ReviewedPostprint (author's final draft

Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs

In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging Technologies for Education. SETE 201

A model for providing emotion awareness and feedback using fuzzy logic in online learning

Monitoring users’ emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students’ attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students’ feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students’ emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students’ learning performance.Peer ReviewedPostprint (author's final draft

A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model