Abstract
We present a recommendation system (RS) which helps users in buying products based on summarizing all the customer reviews of product attributes. Our recommendation system extracts users’ opinions for products through a decision tree. Each node of the tree is a question to the users. From each node, our RS gives a ranked list of products which is matches the opinions of users. We explain (a) a learning tree structure, for instance, at each node which questions can be asked; and (b) producing a suitably ranked list at each node. Firstly, we use a top-down strategy to build a decision tree in order to select the best user attributes corresponding to a question which is asked at each node. Secondly, we use a learning-to-rank method to learn a ranked list of products for each node of the tree. In experimentation, we use amazon datasets for computer products. We evaluate our RS by using mean reciprocal rank (MRR). Experimental results show that RankBoost achieves better quality than RankSVM.
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References
Balakrishnan, S., Chopra, S.: Collaborative ranking. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 143–152. ACM (2012)
Das, M., De Francisci Morales, G., Gionis, A., Weber, I.: Learning to question: leveraging user preferences for shopping advice. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 203–211. ACM (2013)
Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2003)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of the 19th National Conference on Artificial Intelligence, pp. 755–760
Joachims, T.: Training linear SVMS in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226. ACM (2006)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), vol. 1631, p. 1642. Citeseer (2013)
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Nguyen, XH., Nguyen, LM. (2016). Linguistic Features and Learning to Rank Methods for Shopping Advice. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_23
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DOI: https://doi.org/10.1007/978-3-319-49046-5_23
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