Single User-Item Profile Matrix in Recommendations System

Abstract

Recommendation systems play a vital role in assisting users across various domains, including movies, music, books, and products, by providing personalized and relevant item suggestions. One popular approach employed in recommendation systems is the utilization of a utility matrix. This.0 matrix captures user-item interactions, with each cell representing a user's rating or preference for a particular item. By analyzing the utility matrix, recommendation systems can uncover patterns and resemblance between items and users, enabling accurate predictions and personalized recommendations. Collaborative filtering techniques, such as user-based and item-based approaches, are commonly applied to leverage the utility matrix. These techniques exploit collective user preferences similarities to suggest relevant items to users. Furthermore, the incorporation of additional data, such as item attributes or contextual information, and the adoption of hybrid approaches can further enhance recommendation system performance and effectiveness. By harnessing utility matrices and collaborative filtering, recommendation systems deliver tailored recommendations that enrich users' experiences and facilitate the discovery of items aligned with their individual preferences.
 Single User-Item Profile Matrix (SUIPM) is an algorithm which predicts missing feature value like ratings, scores, rankings etc. It uses linear process to predict the missing feature value (like, ratings, scores etc.) of each user within a user cluster or a group of user clusters according to the user’s activity and the preferences. Single User-Item Profile Matrix (SUIPM) algorithm predicts much faster than the Utility Matrix due to its low time complexity. The SUIPM algorithm mainly focuses on the improvement and the optimization of the prediction quality of the Utility Matrix in the recommendations system.