An impact of time and item influencer in collaborative filtering recommendations using graph-based model

• Published in: Information Processing and Management
• Main Author: 2-s2.0-85059583017
• Format: Article
• Language: English
• Published: Elsevier Ltd 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059583017&doi=10.1016%2fj.ipm.2018.12.007&partnerID=40&md5=dce8d529651821a14ef99b43025efdd0

Recommender Systems deal with the issue of overloading information by retrieving the most relevant sources in the wide range of web services. They help users by predicting their interests in many domains like e-government, social networks, e-commerce and entertainment. Collaborative Filtering (CF) is the most promising technique used in recommender systems to give suggestions based on liked-mind users’ preferences. Despite the widespread use of CF in providing personalized recommendation, this technique has problems including cold start, data sparsity and gray sheep. Eventually, these problems lead to the deterioration of the efficiency of CF. Most existing recommendation methods have been proposed to overcome the problems of CF. However, they fail to suggest the top-n recommendations based on the sequencing of the users’ priorities. In this research, to overcome the shortcomings of CF and current recommendation methods in ranking preference dataset, we have used a new graph-based structure to model the users’ priorities and capture the association between users and items. Users’ profiles are created based on their past and current interest. This is done because their interest can change with time. Our proposed algorithm keeps the preferred items of active user at the beginning of the recommendation list. This means these items come under top-n recommendations, which results in satisfaction among users. The experimental results demonstrate that our algorithm archives the significant improvement in comparison with CF and other proposed recommendation methods in terms of recall, precision, f-measure and MAP metrics using two benchmark datasets including MovieLens and Superstore. © 2018 Elsevier Ltd