Abstract
Recommender systems assist the e-commerce providers for services computing in aggregating user profiles and making suggestions tailored to user interests from large-scale data. This is mainly achieved by two primary schemes, i.e., memory-based collaborative filtering and model-based collaborative filtering. The former scheme predicts user interests over the entire large-scale data records and thus are less scalable. The latter scheme is often unsatisfactory in recommendation accuracy. In this paper, we propose Large-scale E-commerce Recommendation Using Smoothing and Fusion (CFSF) for e-commerce providers. CFSF is divided into an offline phase and an online phase. During the offline phase, CFSF creates a global item similarity matrix (GIS) and user clusters, where user ratings within each cluster is smoothed. In the online phase, when a recommendation needs to be made, CFSF dynamically constructs a locally-reduced item-user matrix for the active user item by selecting the top M similar items from GIS and top the K like-minded users from user clusters. Our empirical study shows that CFSF outperforms existing CF approaches in terms of recommendation accuracy and scalability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.