Tokala, S.; Enduri, M.K.; Lakshmi, T.J.; Sharma, H. Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations. Entropy2023, 25, 1360.
Tokala, S.; Enduri, M.K.; Lakshmi, T.J.; Sharma, H. Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations. Entropy 2023, 25, 1360.
Tokala, S.; Enduri, M.K.; Lakshmi, T.J.; Sharma, H. Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations. Entropy2023, 25, 1360.
Tokala, S.; Enduri, M.K.; Lakshmi, T.J.; Sharma, H. Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations. Entropy 2023, 25, 1360.
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, we present a novel parallel computation framework utilizing community information available in the rating network. Our proposed approach named as Community-based Matrix Factorization(CBMF), parallelizes matrix factorization technique by dividing the network into communities using existing community detection algorithms. We prove that this parallel approach not only increases the quality of recommendations in connection with Root Mean Square Error (RMSE), but also yields substantial performance improvement. We empirically evaluate our idea on diverse datasets and present comprehensive experimental results. These results serve as empirical evidence of the effectiveness and performance gains offered by our parallel computation framework.
Keywords
Matrix factorization; Recommendation; Community detection; Parallel computation; RMSE
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
