The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM
This recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem; it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The users ratings are mapped to the relevant item attributes and the users attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.
recommendation algorithm rating matrix cold-start attribute-tables
YIN Hang CHANG Guiran WANG Xingwei
School of Information Science and Engineering, Northeastern University, Shenyang, China Shenyang Aer School of Information Science and Engineering, Northeastern University, Shenyang, China
国际会议
沈阳
英文
1544-1549
2011-11-22（万方平台首次上网日期，不代表论文的发表时间）