Tag boosted hybrid recommendations for multimedia data

Vinod Chhapariya, Sailaja Rajanala, Manish Singh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 6th International Conference on Multimedia Big Data, BigMM 2020
EditorsAnirban Chakraborty, Debashis Sen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781728193250
ISBN (Print)9781728193267
Publication statusPublished - 2020
Externally publishedYes
EventIEEE International Conference on Multimedia Big Data 2020 - New Delhi, India
Duration: 24 Sept 202026 Sept 2020
Conference number: 6th
https://ieeexplore.ieee.org/xpl/conhome/9222464/proceeding (Proceedings)
http://bigmm.midas.iiitd.edu.in/ (Website)


ConferenceIEEE International Conference on Multimedia Big Data 2020
Abbreviated titleBigMM 2020
CityNew Delhi
Internet address


  • Recommendation System
  • Tags
  • Topic Modeling

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