An Enhanced Content-based Filtering Using Maximal Marginal Relevance - Aranzamendez, Samantha Gwyn M.; Bolito, Joshua Caleb D.; Rafe, Aron Christoper R.; Guialil, Jamillah S.; Cortez, Dan Michael A.; Dioses, Raymund M. (International Journal of Computing Sciences Research; Vol 8 (2024): Volume 8, 2024)
Purpose – The study aims to enhance Content-based Filtering by diversifying its recommended items to combat overspecialization. It traditionally recommends items that are directly related to the user profile, preventing users from discovering newer sets of items. Method – Maximal Marginal Relevance is integrated into the algorithm – a re-ranking algorithm, developed by Carbonell and Goldstein that enhances the diversity of items retrieved by information retrieval systems – to enhance Content-based Filtering and address the underlying overspecialization problem. Results – By integrating Maximal Marginal Relevance, the modified algorithm addressed overspecialization. Out of all the tested values of lambda (λ) for MMR, the enhanced Content-based Filtering (CBF-MMR) with λ = 0.7 showed the most prominence, having a good balance between relevance and diversity of recommendations. On average, it improved upon the original algorithm by 48.51% in Precision, 6.40% in Recall, 28.12% in F-Score, and 275.45% in Diversity. Conclusion – Results show that integrating Maximal Marginal Relevance to Content-based Filtering (CBF-MMR) improves the diversity of recommendations. Due to the re-ranking process added by the Maximal Marginal Relevance, the average Precision, Recall, and F-Score also improved. Recommendations – The authors of this study suggest further work on Content-based Filtering with faster re-ranking algorithms, application of the enhanced algorithm to other larger datasets such as GroupLens’ MovieLens 10M dataset, application of the enhanced algorithm to a different domain, and enhancement of the Maximal Marginal Relevance algorithm to be applied in Content-based Filtering. Research Implications – The successful integration of Maximal Marginal Relevance (MMR) in a Content-based Filtering algorithm opens new possibilities for enhancing the diversity and relevance of recommendations of various types of recommender systems. Practical Implications – Beyond the movie recommender system this study was applied to, this study has profound practical implications on other domains that utilize recommender systems including but not limited to the domains of entertainment, e-commerce, and information retrieval platforms.