Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

Abstract

<jats:p>Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| &lt; 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes &lt;10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.</jats:p>

Collaborative Co-Attention Network for Session-Based Recommendation

“…As for future work, on the one hand, we plan to incorporate some external knowledge, e.g., category information and content information, to capture the item relations more accurately [44][45][46][47][48]. For example, some items are complement products to each other and should be recommended together, especially in some e-commerce scenarios.…”

Movie Recommendation System using an Item-based Collaborative Filtering

“…The movie dataset has movie id and genre columns. The rating dataset has the columns: user id, movie id, and rating as shown in Table 2 [3][4][5][6][7].…”

Open-Source Datasets for Recommender Systems Analysis

“…The comparison of datasets using different metrics -users, items, ratings, density, and rating scale is sketched in Table 3 [10][11][12][13][14][15].…”