In:
Advanced Materials Research, Trans Tech Publications, Ltd., Vol. 765-767 ( 2013-9), p. 989-993
Abstract:
This paper presents a novel collaborative filtering recommendation algorithm based on field authorities which simulates the real life word of mouth recommendation mode. It uses the specialistic knowledge from field authorities of different genres, and successfully addresses data sparsity and noise problems existing in traditional collaborative filtering. Meanwhile it also improves prediction accuracy and saves computational overhead effectively. Experiments on MovieLens datasets show that the accuracy of our algorithm is significantly higher than collaborative filtering approach based on experts, and has larger scope because of no external data limitations. Meanwhile, compared to traditional k-NN collaborative filtering, our algorithm has a better performance both in MAE and precision experiments, and the computational overhead has a decrease of 19.2% while they provide the same accuracy level.
Type of Medium:
Online Resource
ISSN:
1662-8985
DOI:
10.4028/www.scientific.net/AMR.765-767
DOI:
10.4028/www.scientific.net/AMR.765-767.989
Language:
Unknown
Publisher:
Trans Tech Publications, Ltd.
Publication Date:
2013
detail.hit.zdb_id:
2265002-7
