Department of Computer Science
Recommending inexperienced products via learning from consumer reviews
Most products in e-commerce are with high cost (e.g., digital cameras, computers) and hence less likely experienced by users (so they are called "inexperienced products"). The traditional recommender techniques (such as user-based collaborative filtering and content-based methods) are thus not effectively applicable in this environment, because they largely assume that the users have prior experiences with the items. In this paper, we have particularly incorporated product reviews to solve the recommendation problem. We first studied how to utilize the reviewer-level weighted feature preferences (as learnt from their written product reviews) to generate recommendations to the current buyer, followed by exploring the impact of Latent Class Regression Models (LCRM) based cluster-level feature preferences (that represent the common preferences of a group of reviewers). Motivated by their respective advantages, a hybrid method that combines both reviewer-level and cluster-level preferences is introduced and experimentally compared to the other methods. The results reveal that the hybrid method is superior to the other variations in terms of recommendation accuracy, especially when the current buyer states incomplete feature preferences. © 2012 IEEE.
inexperienced products, Latent Class Regression Model, product reviews, Recommender system, weighted feature preferences
Source Publication Title
Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops
Wang, Feng, and Li Chen. "Recommending inexperienced products via learning from consumer reviews." Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops (2012): 596-603.