The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM

Abstract:

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 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. 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.

• Periodical: Advanced Materials Research (Volumes 457-458)
• Pages: 1544-1549
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.457-458.1544
• Online since: January 2012
• Authors: Hang Yin, Gui Ran Chang, Xing Wei Wang
• Keywords: Attribute-Tables, Cold Start, Rating Matrix, Recommendation Algorithm

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