Recommendation for New Users with Partial Preferences by Integrating Product Reviews with Static Specifications
Recommending products to new buyers is an important problem for online shopping services, since there are always new buyers joining a deployed system. In some recommender systems, a new buyer will be asked to indicate her/his preferences on some attributes of the product (like camera) in order to address the so called cold-start problem. Such collected preferences are usually not complete due to the user’s cognitive limitation and/or unfamiliarity with the product domain, which are called partial preferences. The fundamental challenge of recommendation is thus that it may be difficult to accurately and reliably find some like-minded users via collaborative filtering techniques or match inherently preferred products with content-based methods. In this paper, we propose to leverage some auxiliary data of online reviewers’ aspect-level opinions, so as to predict the buyer’s missing preferences. The resulted user preferences are likely to be more accurate and complete. Experiment on a real user-study data and a crawled Amazon review data shows that our solution achieves better recommendation performance than several baseline methods.
KeywordsNew users partial preferences product recommendation consumer reviews aspect-level opinion mining static specifications
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- 5.Chen, L., Pu, P.: Users’ eye gaze pattern in organization-based recommender interfaces. In: Proceedings of the 16th International Conference on Intelligent user Interfaces, IUI 2011, pp. 311–314. ACM, New York (2011)Google Scholar
- 7.Edwards, W.: Social utilities. Engineering Economist 6, 119–129 (1971)Google Scholar
- 8.Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation, LREC 2006, pp. 417–422 (2006)Google Scholar
- 10.Ha, V., Haddawy, P.: A hybrid approach to reasoning with partially elicited preference models. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, UAI 1999, pp. 263–270. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
- 12.Levi, A., Mokryn, O., Diot, C., Taft, N.: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. In: Proceedings of the 6th ACM Conference on Recommender Systems, RecSys 2012, New York, NY, USA, pp. 115–122 (2012)Google Scholar
- 16.Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. 3(3), 57:1–57:22 (2012)Google Scholar
- 17.Sindhwani, V., Bucak, S.S., Hu, J., Mojsilovic, A.: A family of non-negative matrix factorizations for one-class collaborative filtering. In: The 1st International Workshop on Recommendation-based Industrial Applications held in the 3rd ACM Conference on Recommender Systems, RecSys: RIA 2009 (2009)Google Scholar
- 19.Wang, F., Chen, L.: Recommendation based on mining product reviews’ preference similarity network. In: The 6th Workshop on Social Network Mining and Analysis, 2012 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SNA-KDD 2012 (2012)Google Scholar
- 20.Wang, F., Chen, L.: Recommending inexperienced products via learning from consumer reviews. In: Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2012, pp. 596–603. IEEE Computer Society, Washington, DC (2012)Google Scholar
- 21.Yates, A., Joseph, J., Popescu, A.-M., Cohn, A.D., Sillick, N.: Shopsmart: product recommendations through technical specifications and user reviews. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 1501–1502. ACM, New York (2008)Google Scholar
- 22.Zhang, W., Ding, G., Chen, L., Li, C., Zhang, C.: Generating virtual ratings from chinese reviews to augment online recommendations. ACM Trans. Intell. Syst. Technol. 4(1) (2013)Google Scholar