Combining social media and location-based services for shop type recommendation

Miao Tian, Bin Guo, Zhiwen Yu, Zhu Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

It is an important yet challenging task for investors to determine the most suitable type of shop (e.g., restaurant, fashion, etc.) for a newly opened store. In this paper, we present a shop type recommendation system, which can be used for multiple parties to make investment decisions. We adopt two types of features, location features and commercial features to model a shop, which are derived from the heterogeneous data, i.e., social media and location-based services (LBS). A novel bias learning matrix factorization method with feature fusion is proposed for recommending an appropriate shop type for a given location. Experimental results show that the proposed method outperforms state-of-the-art solutions.

源语言英语
主期刊名UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers
出版商Association for Computing Machinery, Inc
161-164
页数4
ISBN(电子版)9781450335751
DOI
出版状态已出版 - 7 9月 2015
活动ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015 - Osaka, 日本
期限: 7 9月 201511 9月 2015

出版系列

姓名UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers

会议

会议ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015
国家/地区日本
Osaka
时期7/09/1511/09/15

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