Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

1-2015

Abstract

While automatic travel recommendation has attracted a lot of attentions, the existing approaches generally suffer from different kinds of weaknesses. For example, sparsity problem can significantly degrade the performance of traditional collaborative filtering (CF). If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information. Motivated by this concern, we propose an Author Topic Collaborative Filtering (ATCF) method to facilitate comprehensive Points of Interest (POIs) recommendation for social media users. In our approach, the topics about user preference (e.g., cultural, cityscape, or landmark) are extracted from the textual description of photos by author topic model instead of from GPS (geo-tag). Consequently, unlike CF based approaches, even without GPS records, similar users could still be identified accurately according to the similarity of users’ topic preferences. In addition, ATCF doesn’t pre-define the category of travel topics. The category and user topic preference could be elicited simultaneously. Experiment results with a large test collection demonstrate various kinds of advantages of our approach.

Keywords

Multimedia, Travel Recommendation, Author Topic Model

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

MultiMedia Modeling: 21st International Conference, MMM 2015, Sydney, NSW, Australia, January 5-7, 2015, Proceedings, Part II

Volume

8936

First Page

392

Last Page

402

ISBN

9783319144412

Identifier

10.1007/978-3-319-14442-9_45

Publisher

Springer Verlag

City or Country

Cham

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1007/978-3-319-14442-9_45

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