An Enhanced Memory-Based Collaborative Filtering Algorithm based on User Similarity for Recommender Systems
Ramil G. Lumauag1, Ariel M. Sison2, Ruji P. Medina3
1Ramil G. Lumauag, Technological Institute of the Philippines, Quezon City Campus, Quezon City, Philippines.
2Ariel M. Sison, Emilio Aguinaldo College, Manila, Philippines.
3Ruji P. Medina, Technological Institute of the Philippines, Quezon City Campus, Quezon City, Philippines.
Manuscript received on 25 March 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 18 April 2019 | PP: 778-782 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03520376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The determination of user similarity in a memory-based collaborative filtering is the most crucial part of the process since the result of this will greatly influence the prediction rating in generating an accurate and valuable recommendation. This paper presents an enhanced memory-based collaborative algorithm by formulating a similarity measure to identify the number of co-rated items, compute user similarity by selecting the nearest neighbor. The experimental results on dataset show that the proposed algorithm decreases the Mean Absolute Error and improves the accuracy of the algorithm.
Keywords: Collaborative Filtering Algorithm, Memory-Based Collaborative Filtering, Recommender Systems, User Similarity.
Scope of the Article: Algorithm Engineering