پالایش همکارانه

Recently, the Internet has played a significant and substantial role in people's lives. However, the content available in the global web environment should align with users' daily needs, providing them with useful and up-to-date information tailored to their tastes. In this context, recommender systems assist users by suggesting items that closely match their preferences in less time. Today, with the exponential growth of data, the utilization of recommender systems has surged. Conversely, these systems encounter challenges such as evolving user preferences over time, cold start problem, sparsity within the user-item matrix, the infiltration of fake users in the systems, and their adverse impact on the recommendation lists.The objective of this paper is to propose a recommender system grounded in time and trust factors to enhance the efficiency and precision of system recommendations. Initially, the proposed system addresses the data sparsity dilemma by incorporating reliable implicit ratings into the user-item matrix. Subsequently, it constructs a weighted user-user network based on user rating timestamps and trust relationships among users, thereby mitigating the cold start problem and accounting for changing user preferences over time. The proposed recommender system employs a novel community detection algorithm introduced in this paper to identify the nearest neighbors of active users and recommends the top @k items based on the collaborative filtering approach. Evaluation results of the proposed system, tested on a film recommender system using the Epinions dataset, demonstrate its superior efficiency compared to basic systems.

Given the overwhelming amount of information on the web, users face many options when selecting products or services. Recommender systems build a model based on information from user's past choices and ratings, related or trusted individuals, previously selected products, and the features of such products; the system then prioritize items to recommend them to the user based on this model. Trust aware method will use the trust network between users for estimating products ratings. Researchers have been interested in subject of trust in different facets because of different level of trust in professional fields. This article presents multi-faceted trust model for estimating product ratings, in which users and items are considered due to amount of dependency to each facet and also level of trust in it. Epinions dataset analysis indicates that distance dispersion of users’ choice in a multi-facted trust network is significantly lower than their distribution in a general trust network. Then baseline and similarity base models’ performance have been checked and compared in forms of general and multi-faceted. Model evaluation has been done based on Root Mean Squared Error and Epinions dataset separation in two groups of test and train and also croos validation method. Results indicate that estimation error has been averagely decreased 20% and improve recommender system performance obviously by considering trust component in multi-facted form.