A Collaborative Filtering using SVD on Low-Dimensional Space 


Vol. 10,  No. 3, pp. 273-280, Jun.  2003
10.3745/KIPSTB.2003.10.3.273


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  Abstract

Recommender System can help users to find products to purchase. A representative method for recommender systems is collaborative filtering (CF). It predict products that user may like based on a group of similar users. User information is based on user´s ratings for products and similarities of users are measured by ratings. As user is increasing tremendously, the performance of the pure collaborative filtering is lowed because of high dimensionality and scarcity of data. We consider the effect of dimension deduction in collaborative filtering to cope with scarcity of data experimentally. We suggest that SVD improves the performance of collaborative filtering in comparison with pure collaborative filtering.

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[IEEE Style]

J. Jeong and P. K. Rhee, "A Collaborative Filtering using SVD on Low-Dimensional Space," The KIPS Transactions:PartB , vol. 10, no. 3, pp. 273-280, 2003. DOI: 10.3745/KIPSTB.2003.10.3.273.

[ACM Style]

Jun Jeong and Pil Kyu Rhee. 2003. A Collaborative Filtering using SVD on Low-Dimensional Space. The KIPS Transactions:PartB , 10, 3, (2003), 273-280. DOI: 10.3745/KIPSTB.2003.10.3.273.

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