Improving Entity Recommendation with Search Log and Multi-Task Learning

• DOI: 10.24963/ijcai.2018/571
• Publication Date: 2018-07-05T01:49:10Z
• Authors: Jizhou Huang; Wei Zhang; Yaming Sun; Haifeng Wang; Ting Liu
• Keywords: Semantic Search; Learning to Rank

Entity recommendation, providing search users with an improved experience by assisting them in finding related entities for a given query, has become indispensable feature of today's Web engine. Existing studies typically only consider the query issued at current time step while ignoring in-session preceding queries. Thus, they fail to handle ambiguous queries such as "apple" because model could not understand which apple (company or fruit) is talked about. In this work, we believe that contexts convey valuable evidences facilitate semantic modeling queries, and take into consideration entity recommendation. Furthermore, order better semantics learn multi-task learning setting where representation shared across recommendation context-aware ranking. We evaluate our approach using large-scale, real-world logs widely used commercial The experimental results show incorporating context information significantly improves bring further improvements.