Query Embedding Learning for Context-based Social Search

Cheng Te Li, Yi Chun Chen

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 2019 Jan 1
Event2018 Conference on Information and Knowledge Management Workshops, CIKM 2018 - Torino, Italy
Duration: 2018 Oct 22 → …

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


Dive into the research topics of 'Query Embedding Learning for Context-based Social Search'. Together they form a unique fingerprint.

Cite this