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Application of Multi-Attribute Rating Matrix in Cold-start Recommendation

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5 Author(s)
Yin Hang ; Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China ; Guiran, Chang ; Wang Xingwei ; Wu Jiehong
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This recommendation algorithm based on User-Item Rating Matrix is inefficient in the case of cold-start. The Application of Multi-Attribute Rating Matrix (MARM) can solve the problem effectively. The user and item information are analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix (UAIARM). 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.

Published in:

Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on

Date of Conference:

13-15 Dec. 2010

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