Abstract
Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.
Highlights
The exponential increase of data in the digital universe has encouraged efficient information filtering and personalization technology
The test samples are given to the related models such as M1, M2, and M3 and the prediction values are found by using Mean Absolute Error (MAE)
The experiment is performed without applying the proposed model, and the MAE values are tabulated in Tab. 7, which proves that the proposed model reduces the error value
Summary
The exponential increase of data in the digital universe has encouraged efficient information filtering and personalization technology. Recommender System (RS) is a popular technique to perform both information filtering and personalization to the end-user from the huge information space. LinkedIn, etc., to provide more relevant and personalized suggestions. Tapestry is the oldest recommendation system that filters the mail, which is interested in the user [1]. RS is broadly divided into three types namely (i) Collaborative Filtering (CF) (ii) Content-Based Filtering (CBF) and (iii) Hybrid CF recommendation systems that finds relevant items by finding the users having similar interests [3]. Content-Based Filtering (CBF) suggests items that are similar in features and the user has already chosen in the past [4]. Vekariya et al [5] mentioned the hybrid system types given by Robert Burke: weighted, switching, mixed technique, feature combination cascade, feature augmentation, and meta-level. There are three approaches in RS in that CF is successful in research and practice
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