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A platform for research: civil engineering, architecture and urbanism
A fast collaborative filtering algorithm for implicit binary data
Item-based and user-based collaborative filtering are two well-known algorithms for recommender system in E-commerce. Both the algorithms make use of similarity matrix whose elements represent the similarity of each item pairs or user pairs. A fast algorithm for item-based similarity matrix computation using cosine similarity metric was reviewed and applied for user-based one with some modification. The results show that the fast algorithm can blend well with other similarity metrics, and it can greatly improve the computational performance.
A fast collaborative filtering algorithm for implicit binary data
Item-based and user-based collaborative filtering are two well-known algorithms for recommender system in E-commerce. Both the algorithms make use of similarity matrix whose elements represent the similarity of each item pairs or user pairs. A fast algorithm for item-based similarity matrix computation using cosine similarity metric was reviewed and applied for user-based one with some modification. The results show that the fast algorithm can blend well with other similarity metrics, and it can greatly improve the computational performance.
A fast collaborative filtering algorithm for implicit binary data
Manzhao Bu, (author) / Shijian Luo, (author) / Ji He, (author)
2009-11-01
109080 byte
Conference paper
Electronic Resource
English
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