西华大学学报（自然科学版）Issue(4)：8-12,27,6.DOI:10.3969/j.issn.1673-159X.2015.04.002
一种融合用户偏好与信任度的增强协同过滤推荐方法
An Improved Collaborative Filtering Recommendation Method with User Trust-Preference Fusion
摘要
Abstract
Collaborative filtering ( CF) is the most popular recommendation technique but still suffers from data sparisity and cold-start problems. Shambour proposed a trust-semantic fused ( TSF) hybrid recommender approach, which incorporated additional infor-mation from the users’ social trust network and the items’ semantic domain knowledge to alleviate these problems, but it involves large computation. In this paper we improved the user-based trust enhanced CF algorithm therein. By introducing a weighting combination parameter we merge the user trust weighted rating and the user preference weighted rating together to obtain the overall rating predic-tion. Simulation results under the Movielens datasets show the proposed method is superior to the baseline algorithms in terms of mean absolute error ( MAE) .关键词
信任/协同过滤/推荐系统/相似度/MAEKey words
trust/collaborative filtering/recommendation system/similarity/MAE分类
信息技术与安全科学引用本文复制引用
范永全,杜亚军,成丽静,朱爱云..一种融合用户偏好与信任度的增强协同过滤推荐方法[J].西华大学学报（自然科学版）,2015,(4):8-12,27,6.基金项目
教育部春晖计划(Z2011088) （Z2011088）
四川省教育厅重点项目(11ZB002) （11ZB002）
四川省高校重点实验室基金(SZJJ2012-027, SZJJ2014-033) （SZJJ2012-027, SZJJ2014-033）
西华大学重点科研基金项目(Z1412620)。 （Z1412620）
