Query Embedding Learning for Context-based Social Search

Cheng Te Li, Yi Chun Chen

研究成果: Conference article同行評審

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
期刊CEUR Workshop Proceedings
2482
出版狀態Published - 2019 一月 1
事件2018 Conference on Information and Knowledge Management Workshops, CIKM 2018 - Torino, Italy
持續時間: 2018 十月 22 → …

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)

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