Generating Recommendation Evidence Using Translation Model / 2810
Jizhou Huang, Shiqi Zhao, Shiqiang Ding, Haiyang Wu, Mingming Sun, Haifeng Wang
Entity recommendation, providing entity suggestions relevant to the query that a user is searching for, has become a key feature of today's web search engine. Despite the fact that related entities are relevant to users' search queries, sometimes users cannot easily understand the recommended entities without evidences. This paper proposes a statistical model consisting of four sub-models to generate evidences for entities, which can help users better understand each recommended entity, and figure out the connections between the recommended entities and a given query. The experiments show that our method is domain independent, and can generate catchy and interesting evidences in the application of entity recommendation.