Content-aware collaborative filtering for location recommendation based on human mobility data

Defu Lian, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, Xing Xie, Tao Zhou, Yong Rui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations

Abstract

Location recommendation plays an essential role in helping people find places they are likely to enjoy. Though some recent research has studied how to recommend locations with the presence of social network and geographical information, few of them addressed the cold-start problem, specifically, recommending locations for new users. Because the visits to locations are often shared on social networks, rich semantics (e.g., tweets) that reveal a person's interests can be leveraged to tackle this challenge. A typical way is to feed them into traditional explicit-feedback content-aware recommendation methods (e.g., LibFM). As a user's negative preferences are not explicitly observable in most human mobility data, these methods need draw negative samples for better learning performance. However, prior studies have empirically shown that sampling-based methods don't perform as well as a method that considers all unvisited locations as negative but assigns them a lower confidence. To this end, we propose an Implicit-feedback based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and steer clear of negative sampling. For efficient parameter learning, we develop a scalable optimization algorithm, scaling linearly with the data size and the feature size. Furthermore, we offer a good explanation to ICCF, such that the semantic content is actually used to refine user similarity based on mobility. Finally, we evaluate ICCF with a large-scale LBSN dataset where users have profiles and text content. The results show that ICCF outperforms LibFM of the best configuration, and that user profiles and text content are not only effective at improving recommendation but also helpful for coping with the cold-start problem.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages261-270
Number of pages10
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Externally publishedYes
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period11/14/1511/17/15

Keywords

  • Content aware
  • Implicit feedback
  • Location recommendation

ASJC Scopus subject areas

  • Engineering(all)

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