PERSONALIZED SUMMARIZATION OF CUSTOMER REVIEWS BASED ON USER'S BROWSING HISTORY

Every e-commerce web site today has the product review feature which allows customers to express their opinions and comments about the product they have purchased. These comments are important for potential customers when deciding which product to buy. However, reading large amounts of customer reviews available for each product is a time consuming process. For this reason, customers usually tend to read small pieces of topmost comments and skip the rest of them. Also, depending on personal preferences and needs, customers might be interested in different features of various products. Therefore, a feature based summarization of the products is very helpful for potential customers in selecting the best product option. Existing feature based review summarization methods create a product summary for a common user profile ignoring the individual preferences. In this paper, we propose a novel feature based approach for personalized review summarization by giving importance to potential individual customer preferences. In order to evaluate our method, a dataset has been collected from a popular Turkish e-commerce web site. The experimental results show that our method is successful in finding and summarizing the most relevant reviews for the active user.