Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations

TOKALA, Srilatha, ENDURI, Murali Krishna, LAKSHMI, T. Jaya and SHARMA, Hemlata (2023). Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations. Entropy, 25 (9): 1360.

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Abstract

Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone.

Item Type:	Article
Additional Information:	** Article version: VoR ** From MDPI via Jisc Publications Router ** Licence for VoR version of this article: https://creativecommons.org/licenses/by/4.0/ ** Peer reviewed: TRUE **Journal IDs: eissn 1099-4300 **Article IDs: publisher-id: entropy-25-01360 **History: published_online 20-09-2023; accepted 13-09-2023; rev-recd 08-09-2023; collection 09-2023; submitted 01-08-2023
Uncontrolled Keywords:	community detection, rating network, matrix factorization, RMSE, recommender system
Identification Number:	https://doi.org/10.3390/e25091360
SWORD Depositor:	Colin Knott
Depositing User:	Colin Knott
Date Deposited:	12 Oct 2023 13:48
Last Modified:	12 Oct 2023 14:00
URI:	https://shura.shu.ac.uk/id/eprint/32530

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