Ensemble Based Hybrid Recommender Systems
T. Prathima1, B. Anjana2, V. Apoorva3, B.R.Sridhar4
1T. Prathima*, Assistant Professor, Dept. of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India.
2B. Anjana, Dept. of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India.
3V.Apoorva, Dept. of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India.
4B.R. Sridhar, Assistant Professor, Dept. of Mathematics & Humanities, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India.
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 826-833 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8460019320/2020©BEIESP | DOI: 10.35940/ijitee.C8460.019320
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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: In the past few years, the advent of computational and prediction technologies has spurred a lot of interest in recommendation research. Content-based recommendation and collaborative filtering are two elementary ways to build recommendation systems. In a content based recommender system, products are described using keywords and a user profile is developed to enlist the type of products the user may like. Widely used Collaborative filtering recommender systems provide recommendations based on similar user preferences. Hybrid recommender systems are a blend of content-based and collaborative techniques to harness their advantages to maximum. Although both these methods have their own advantages, they fail in ‘cold start’ situations where new users or products are introduced to the system, and the system fails to recommend new products as there is no usage history available for these products. In this work we work on MovieLens 100k dataset to recommend movies based on the user preferences. This paper proposes a weighted average method for combining predictions to improve the accuracy of hybrid models. We used standard error as a measure to assign the weights to the classifiers to approximate their participation in predicting the recommendations. The cold start problem is addressed by including demographic data of the user by using three approaches namely Latent Vector Method, Bayesian Weighted Average, and Nearest Neighbor Algorithm.
Keywords: Bayesian Weighted Average, Cold start, Hybrid Recommender System, Ensemble Hybrid Models, Latent Vector Method, Nearest Neighbor Algorithm
Scope of the Article: Algorithm Engineering