Pradnya Bhagat
ABSTRACT
The Recommender systems technology is being massively exploited by e-commerce giants to enhance the shopping experience of their clients which in turn helps in improving the sales of the company. Most of the recommender systems in use today are based on Collaborative Filtering (CF) in which the known preferences of a group of users are used to make recommendations or predictions for the unknown preferences of other users. Although these ratings communicate about the quality of the product, they almost most of the times fail to express the reason behind people believing the product to be of a particular quality. This information can be inferred if analyze the information rich textual reviews written by the users.
In the current work, an attempt is made to study and implement various methods described in literature, to mine the product features from the user reviews associated with the product. A comparative study is presented at the end to appreciate the performance of the methods.
- Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference Santiago, Chile (1994). Google Scholar
Digital Library
- Ruihai Dong, Kevin McCarthy, Michael P. O'Mahony, Markus Schaal, and Barry Smyth. 2012. Towards an Intelligent Reviewer's Assistant: Recommending Topics to Help Users to Write Better Product Reviews. IUI'12, Lisbon, Portugal (2012). Google ScholarDigital Library
- Ruihai Dong, Markus Schaal, Kevin McCarthy Michael P. O'Mahony, and Barry Smyth. 2012. Unsupervised Topic Extraction for the Reviewer's Assistant. Springer-Verlag London (2012).Google Scholar
- Ruihai Dong, Markus Schaal, Michael P. O'Mahony, and Barry Smyth. 2013. Topic Extraction from Online Reviews for Classification and Recommendation. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (2013). Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with oneclass collaborative filtering. WWW (2016). Google ScholarDigital Library
- Minqing Hu and Bing Liu. 2004. Mining and Summarizing Customer Reviews. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004) (2004). Google ScholarDigital Library
- Cane Wing ki Leung, Stephen Chi-fai, Chan Fu-lai, and Chung Grace Ngai. 2011. A probabilistic rating inference framework for mining user preferences from reviews. World Wide Web (2011). Google ScholarDigital Library
- Raksha Sharma, Mohit Gupta, Astha Agarwal, and Pushpak Bhattacharyya. 2015. Adjective Intensity and Sentiment Analysis. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (2015).Google ScholarCross Ref
- Ann Taylor, Mitchell Marcus, and Beatrice Santorini. 2003. The Penn Treebank: An Overview. In: Abeille A. (eds) Treebanks. Text, Speech and Language Technology, vol 20. Springer, Dordrecht (2003).Google ScholarCross Ref
- Kristina Toutanova, Dan Klein, Christopher Manning,, and Yoram Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLTNAACL (2003). Google ScholarDigital Library
- Lei Zhang and Bing Liu. 2011. Identifying Noun Product Features that Imply Opinions. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:shortpapers (2011). Google ScholarDigital Library
Index Terms
- A comparative study of feature extraction methods from user reviews for recommender systems
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