Personalized Movie Recommendation Based on Social Tagging Systems

DOI

10.2991/icadme-17.2017.78How to use a DOI?

Keywords

Social Tagging Systems, Movie Recommendation, Similarity Calculation, Personalized Recommendation

Abstract

In the Internet era, the movie website has become a mainstream platform for movies introduction and comment on the movie resources, annotation also has become a mainstream form of movie resources under the network environment of the new organization based on social tagging.. From the system point of view, the greatest advantage offered by tagging applications is the richness of the tag profiles. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag ambiguity hindering navigation. Thus, a key question is how to harvest tag semantics from these systems. We present an algorithm of tags clustering. With this algorithm, we clustering the tags into a semantic tree, then we turn every movie item into an induced tree. We propose a new method for movie recommendation, which based on semantic similarity of tags. When we retrieve the movie based on semantic similarity of tags, our algorithm shows the high precision.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lin Wang
PY  - 2017/07
DA  - 2017/07
TI  - Personalized Movie Recommendation Based on Social Tagging Systems
BT  - Proceedings of the 2017 7th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 412
EP  - 416
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-17.2017.78
DO  - 10.2991/icadme-17.2017.78
ID  - Wang2017/07
ER  -