{"type":"ScholarlyArticle","title":"Improving Entity Recommendation with Search Log and Multi-Task Learning","authors":[{"name":"Jizhou Huang","slug":"jizhou-huang","given":"Jizhou","family":"Huang","affiliation":"† Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Wei Zhang","slug":"wei-zhang","given":"Wei","family":"Zhang","affiliation":"‡ Baidu Inc. , Beijing , China"},{"name":"Yaming Sun","slug":"yaming-sun","given":"Yaming","family":"Sun","affiliation":"‡ Baidu Inc. , Beijing , China"},{"name":"Haifeng Wang","slug":"haifeng-wang","given":"Haifeng","family":"Wang","affiliation":"‡ Baidu Inc. , Beijing , China"},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":"† Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"}],"abstract":"Entity recommendation, providing search users with an improved experience by assisting them in finding related entities for a given query, has become an indispensable feature of today's Web search engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as \"apple\" because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking.  We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements.","doi":"10.24963/ijcai.2018/571","url":"https://ijcai.org/proceedings/2018/571","pdf":"https://ijcai.org/proceedings/2018/0571.pdf","markdown":"https://ijcai.org/proceedings/2018/571.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/571","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":571,"pages":{"first":4107,"last":4114},"keywords":["Natural Language Processing: Natural Language Processing","Machine Learning: Transfer, Adaptation, Multi-task Learning","Natural Language Processing: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Bar-Yossef and Kraus, 2011] Ziv Bar-Yossef and Naama Kraus. 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