A new improved KNN-based recommender system

Despite all the developments in recommender systems and utilizations of successful application models in the industry, it can be said that there is still a need to improve various parts of these systems in order to enhance their effectiveness and scope of application. Recommender Systems (RS) are well-known in the field of e-commerce and are expected to provide customers with important goods and items (including music and movies). In traditional recommender systems suffer from important challenges and problems such as cold start, scalability, and data dispersion. Recently, some of these challenges have been successfully overcomed to an acceptable extent by the advantages of the combined use of these methods. The K-nearest neighbors (KNN)-based Recommender Systems (KRS) are among the most powerful recommender engines that are currently available. In these systems, the rating of a target item is predicted based on the average rating of similar items, where the similarity is defined based on a similarity measure and the average rating of items is treated as a feature. In this paper, KRS is developed by combining the following approaches: (a) using the mean and variance of the item ratings as item features to identify the similar items (i.e., item-based KRS or IKRS); (b) using the mean and variance of the user ratings as the user features to identify the similar users (i.e., user-based KRS or UKRS); (c) using weighted average to combine the neighboring user/item ratings; and (d) using ensemble learning. In this study, some methods, i.e., EVMRS (Ensemble Variance-Mean based Recommender System) and EWVMRS (Ensemble Weighted Variance-Mean based Recommender System) are discussed and an improved EWVMRSG (Ensemble Weighted Variance-Mean Based Recommender system enriched by Gaussian mixture model (GMM)) is expanded, which are all user-based and involve using Mean Distance as the measure of similarity between the users/items to find neighboring users/items, but then they use unweighted average, weighted average, and weighted averaging based on the full-covariance GMM, respectively, for prediction. Experimental evaluations show that the EVMRS, EWVMRS, and EWVMRSG methods, which all use ensemble learning, are the most accurate methods among those developed and evaluated in this study. Depending on the dataset, the EWVMRSG mothed achieves a 20-30% lower absolute error than that of the original Mean based Recommender Systems (MRS). In terms of execution time, the proposed method is comparable to the original MRS models and is much faster than the Slope-one, P-kNN, and C-kNN RSs.