Tag Boosted Hybrid Recommendation System for Multimedia Data

Chhapariya, Vinod and Singh, Manish (2018) Tag Boosted Hybrid Recommendation System for Multimedia Data. Masters thesis, Indian Institute of Technology Hyderabad.

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Collaborative recommendation systems are more popular for multimedia data compared to content- based recommendation system. Existing content-based recommendation algorithms give low-quality recommendations for multimedia data due to lack of good content-based features. Collaborative algorithms do well only when suficient user history is available. However, it gives very poor perfor- mance compared to content-based algorithms for new users or new items due to lack of user history. To get best of both, one can use hybrid recommendation system that integrates content and collab- orative algorithms. For multimedia data, it is di�cult to build a hybrid recommendation system as relevant content based features are not easily available. With the advent ofWeb 2.0, a lot of feedback from users on these multimedia objects is available in the form of tags, reviews, likes, comments, etc. In this paper, we propose a hybrid recommendation system that uses features from such social interactions. We do collaborative filtering using probabilistic matrix factorization and content-based filtering using topic modeling and integrate them using Bayesian model. Extensive experiments on real-world dataset show that our algorithm significantly improves the recommendation performance for multimedia data.

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IITH Creators:
IITH CreatorsORCiD
Singh, Manishhttp://orcid.org/0000-0001-5787-1833
Item Type: Thesis (Masters)
Uncontrolled Keywords: Multifunctional Sensors
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 26 Jun 2018 07:07
Last Modified: 26 Jun 2018 07:07
URI: http://raiith.iith.ac.in/id/eprint/4078
Publisher URL:
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