Bi-MARS: A Bi-clustering based Memetic Algorithm for Recommender Systems

Abstract Context: With the expansion of e-business, availability of items on the web is profuse as compared to the previous era. The task of finding relevant items from this pool of items available online is a time-consuming task. Collaborative Filtering (CF) is the foremost productive recommendation algorithm that helps the user find relevant items and thus increase the user’s engagement. However, several drawbacks of CF, especially data sparsity, scalability, and relevance of items recommended to users are open research issues that pose serious challenges to precision of the algorithm. Objective: We proposed a novel Bi-clustering based Memetic Algorithm for Recommender Systems (Bi-MARS) based on the collaborative behavior of memes. Method: Bi-clusters are created for discovering precise and localized neighborhood of the target user. Further, a novel local search to refine similarity values associated with neighborhood users and prediction score function for unrated items are formulated. Results: We evaluated the performance of Bi-MARS on MovieLens dataset of three different sizes. Additionally, results are compared with eight other approaches namely, traditional CF, Probabilistic Latent Semantic Analysis, Non-negative Matrix Factorization, Entropy-based CF, Jaccard Coefficient-based Bi-clustering and Fusion, Combined Bi-clustering and Entropy-based CF (CBE-CF), and evolutionary approaches: Genetic Algorithm based Recommender System and Memetic Algorithm based Recommender System (MARS). Experimental results depict that Bi-MARS outperforms all above-said approaches. The precision of MARS is improved by 66.3% on Movielens dataset of size 100K, which is the most precise algorithm after Bi-MARS among all evolutionary algorithms considered. Further, CPU Time of CBE-CF, which portrays least mean absolute error, among all clustering techniques considered, is improved by 53.8% with Bi-MARS. Conclusion: Bi-MARS generates recommendations by finding the closest similarity vector of the target user, which contributes to the computational accuracy of the algorithm and thus the relevance of items.