Retrieval of Relevant Opinion Sentences for New Products

Dae Hoon Park, Hyun Duk Kim, ChengXiang Zhai, Lifan Guo
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
With the rapid development of Internet and E-commerce, abundant product reviews have been written by consumers who bought the products. These reviews are very useful for consumers to optimize their purchasing decisions. However, since the reviews are all written by consumers who have bought and used a product, there are generally very few or even no reviews available for a new product or an unpopular product. We study the novel problem of retrieving relevant opinion sentences from the reviews
more » ... other products using specifications of a new or unpopular product as query. Our key idea is to leverage product specifications to assess product similarity between the query product and other products and extract relevant opinion sentences from the similar products where a consumer may find useful discussions. Then, we provide ranked opinion sentences for the query product that has no user-generated reviews. We first propose a popular summarization method and its modified version to solve the problem. Then, we propose our novel probabilistic methods. Experiment results show that the proposed methods can effectively retrieve useful opinion sentences for products that have no reviews.
doi:10.1145/2766462.2767748 dblp:conf/sigir/ParkKZG15 fatcat:skbb2x2mjfcd3co5y2i5ookjn4