Generating Personalized Snippets for Web Page Recommender Systems

Web page recommender systems provide users with web pages they might be interested in. Users then select some of the recommended web pages that catch their interest by making a relevance judgment. However, if web page recommender systems do not offer enough useful information for the relevance judgment, users would end up reading irrelevant web pages or overlook relevant web pages. To provide information for the relevance judgment, we propose a novel method for generating personalized snippets for web page recommender systems. Our method directly uses the reasons the web pages are recommended to the user. This use of reasons enables snippets to be selected that better reflect the interest of the user. Moreover, our method can work with various web page recommender systems. It also leverages the maximum coverage summarization model to generate personalized snippets. The results of an experiment on a manually created dataset show that our method is more effective than a personalized summarization model.