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With development of network technology and society, people enjoy the e-commerce shopping convenience while also deeply troubled by the "information overload" problem. Recommendation systems help the customers find suitable products they need from a large number of commodities. Among the recommendation systems, the most widely used algorithm is collaborative filtering (CF) recommendation...
Collaborative filtering is the state-of-the-art and widely applied method in personalized recommendation systems. However, the problem of precision resulting from sparsity exists chronically. To address the issue, we develop collaborative filtering algorithm that incorporates the variance analysis of attributes-value preference, which can improve recommending precision further. What we operate on...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.