Aspect Selection for Social Recommender Systems

In this paper, we extend our previous work on social recommender systems to harness knowledge from product reviews. By mining product reviews, we can exploit sentiment-rich content to ascertain user opinion expressed over product aspects. Aspect aware sentiment analysis provides a more structured approach to product comparison. However, aspects extracted using NLP-based techniques remain too large and lead to poor quality product comparison metrics. To overcome this problem, we explore the utility of feature selection heuristics based on frequency counts and Information Gain (IG) to rank and select the most useful aspects. Here an interesting contribution is the use of top ranked products from Amazon to formulate a binary classification over products to form the basis for the supervised IG metric. Experimental results on three related product families (Compact Cameras, DSLR Cameras and Point & Shoot Cameras) extracted from Amazon.com demonstrate the effectiveness of incorporating feature selection techniques for aspect selection in recommendation task.

[1]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[2]  Oren Etzioni,et al.  RevMiner: an extractive interface for navigating reviews on a smartphone , 2012, UIST.

[3]  Royi Ronen,et al.  Selecting content-based features for collaborative filtering recommenders , 2013, RecSys.

[4]  Meng Wang,et al.  Product Aspect Ranking and Its Applications , 2014, IEEE Transactions on Knowledge and Data Engineering.

[5]  Blanca Vargas-Govea,et al.  Effects of relevant contextual features in the performance of a restaurant recommender system , 2011 .

[6]  Martin Ester,et al.  On the design of LDA models for aspect-based opinion mining , 2012, CIKM.

[7]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[8]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[9]  Nirmalie Wiratunga,et al.  Contextual Sentiment Analysis in Social Media Using High-Coverage Lexicon , 2013, SGAI Conf..

[10]  Enric Plaza,et al.  Sentiment and Preference Guided Social Recommendation , 2014, ICCBR.

[11]  Markus Schaal,et al.  Opinionated Product Recommendation , 2013, ICCBR.

[12]  Brendan T. O'Connor,et al.  Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters , 2013, NAACL.

[13]  Daniel Borrajo,et al.  Case-Based Recommendation of Node Ordering in Planning , 2007, FLAIRS Conference.

[14]  Li Chen,et al.  Recommender systems based on user reviews: the state of the art , 2015, User Modeling and User-Adapted Interaction.

[15]  Barry Smyth,et al.  Case-Based Recommendation , 2007, The Adaptive Web.

[16]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[17]  Ivan Koychev,et al.  Feature Selection and Generalisation for Retrieval of Textual Cases , 2004, ECCBR.