CAJ | 학술논문

协同过滤是一种最流行的推荐技术，但仍然受到数据稀疏和冷启动问题的困扰。针对Shambour提出的信任—语义融合( TSF)的混合推荐方法中计算量较大的问题，提出一种融合用户偏好与信任度的增强协同过滤推荐方法。该方法在计算评分预测时，对基于用户的信任增强协同过滤算法进行改进，先将用户相似度和信任度分别进行近邻选择和加权评分，再通过一个加权因子对2部分进行融合，从而得到总体的预测评分。在Movielens数据集下进行仿真验证，其结果表明，与基准算法相比，本文方法具有更好的MAE性能。
협동과려시일충최류행적추천기술，단잉연수도수거희소화랭계동문제적곤우。침대Shambour제출적신임—어의융합( TSF)적혼합추천방법중계산량교대적문제，제출일충융합용호편호여신임도적증강협동과려추천방법。해방법재계산평분예측시，대기우용호적신임증강협동과려산법진행개진，선장용호상사도화신임도분별진행근린선택화가권평분，재통과일개가권인자대2부분진행융합，종이득도총체적예측평분。재Movielens수거집하진행방진험증，기결과표명，여기준산법상비，본문방법구유경호적MAE성능。
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) .