Bring User Interest to Related Entity Recommendation

Abstract

Most existing approaches to query recommendation focus on query-term or click based analysis over the user session log or click-through data. For entity query, however, finding the relevant queries from these resources is far from trivial. Entity query is a special kind of short queries that commonly appear in image search, video search or object search. Focusing on related entity recommendation, this paper proposes to collect rich related entities of interest from a large number of entity-oriented web pages. During the collection, we maintain a large-scale and general-purpose related entity network (REN), based upon a special co-occurrence relation between the related entity and target entity. Benefiting from the REN, we can easily incorporate various types of related entity into recommendation. Different ranking methods are employed to recommend related and diverse entities of interest. Extensive experiments are conducted to assess the recommendation performance in term of Accuracy and Serendipity. Experimental results show that the REN is a good recommendation resource with high quality of related entities. For recommending related entity, the proposed REN-based method achieves good performance compared with a state-of-the-art relatedness measurement and two famous recommendation systems.