Query embedding learning for context-based social search

Yi Chun Chen, Yu Che Tsai, Cheng Te Li

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

Recommending individuals through keywords is an essential and common search task in online social platforms such as Facebook and LinkedIn. However, it is often that one has only the impression about the desired targets, depicted by labels of social contexts (e.g. gender, interests, skills, visited locations, employment, etc). Assume each user is associated a set of labels, we propose a novel task, Search by Social Contexts (SSC), in online social networks. SSC is a kind of query-based people recommendation, recommending the desired target based on a set of user-specified query labels. We develop the method Social Query Embedding Learning (SQEL) to deal with SSC. SQEL aims to learn the feature representation (i.e., embedding vector) of the query, along with user feature vectors derived from graph embedding, and use the learned query vectors to find the targets via similarity. Experiments conducted on Facebook and Twitter datasets exhibit satisfying accuracy and encourage more advanced efforts on search by social contexts.

原文English
主出版物標題CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
發行者Association for Computing Machinery
頁面2441-2444
頁數4
ISBN(電子)9781450369763
DOIs
出版狀態Published - 2019 十一月 3
事件28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
持續時間: 2019 十一月 32019 十一月 7

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
國家/地區China
城市Beijing
期間19-11-0319-11-07

All Science Journal Classification (ASJC) codes

  • 決策科學 (全部)
  • 商業、管理和會計 (全部)

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