Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.
|Number of pages||1|
|Journal||Proceedings of the Annual Hawaii International Conference on System Sciences|
|State||Published - 10 Nov 2005|
|Event||38th Annual Hawaii International Conference on System Sciences - Big Island, HI, United States|
Duration: 3 Jan 2005 → 6 Jan 2005