Tag Boosted Hybrid Recommendations for Multimedia Data

Chhapariya, Vinod and Rajanala, Sailaja and Singh, Manish (2020) Tag Boosted Hybrid Recommendations for Multimedia Data. In: Proceedings - 2020 IEEE 6th International Conference on Multimedia Big Data, BigMM 2020, 24 September 2020 - 26 September 2020.

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Abstract

Multimedia data is known for its variety and also for the difficulty that comes in extracting relevant features from multimedia data. Owing to which the collaborative recommendation systems have found their foothold in multimedia recommender systems. However, modern-day multimedia sites have tons of user history in the form of user feedback, reviews, votes, comments, and etc. We can use these social interactions to extract useful content features, which can then be used in content based recommendation system. In this paper, we propose a novel hybrid recommender system that combines the content and collaborative systems using a Bayesian model. We substitute the concrete textual content with a sparse tag information. Extensive experiments on real-world dataset show that tags significantly improves the recommendation performance for multimedia data.

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IITH Creators:

IITH Creators	ORCiD
Chhapariya, Vinod	UNSPECIFIED
Rajanala, Sailaja	UNSPECIFIED
Singh, Manish	http://orcid.org/0000-0001-5787-1833

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Collaborative recommendation system; Collaborative systems; Content-based recommendation; Hybrid recommendation; Hybrid recommender systems; Multimedia recommender; Recommendation performance; Social interactions

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

09 Jul 2021 10:26

Last Modified:

09 Jul 2021 10:26

URI:

http://raiithold.iith.ac.in/id/eprint/8199