CAJ | 학술논문

协同过滤推荐是目前应用最为广泛的推荐策略之一,但存在数据稀疏和难扩展问题.文中在传统基于用户的协同过滤推荐算法的基础上,引入信任关系计算,利用信任关系的条件传递特性,设计并构建一个集用户声望信任和用户局部信任的混和信任网络,并将用户间评分相似度和网络中用户间信任评价度结合,为用户寻找更多基于信任因素和兴趣因素的二维相似近邻.在Epinions数据集上以平均绝对误差( MAE)和均方根误差( RSME)等作为实验评价指标,对该方法进行验证实验.结果表明相比传统协同过滤推荐算法,该方法在 MAE 上提高约6.8%,最优值达到0.7513,t检验的结果也表明该方法能显著提高推荐系统性能.
협동과려추천시목전응용최위엄범적추천책략지일,단존재수거희소화난확전문제.문중재전통기우용호적협동과려추천산법적기출상,인입신임관계계산,이용신임관계적조건전체특성,설계병구건일개집용호성망신임화용호국부신임적혼화신임망락,병장용호간평분상사도화망락중용호간신임평개도결합,위용호심조경다기우신임인소화흥취인소적이유상사근린.재Epinions수거집상이평균절대오차( MAE)화균방근오차( RSME)등작위실험평개지표,대해방법진행험증실험.결과표명상비전통협동과려추천산법,해방법재 MAE 상제고약6.8%,최우치체도0.7513,t검험적결과야표명해방법능현저제고추천계통성능.
Collaborative filteration is one of the most widely used recommendation strategies, in which data sparsity problem and expansion difficulty exist. Based on traditional user-based collaborative filtering algorithms, the trust computation is introduced into the process of recommendation. Making full use of the propagation characteristics of trust relationship under some conditions, a hybrid network composed of the user reputation-trust and the user local-trust is designed and built. And the user rating similarity is combined with trust evaluation of the hybrid network, which helps users to discover more two-dimensional similarity neighbors based on trust and interest factors. The proposed method is validated by the experiment on Epinions dataset with Mean Absolute Error ( MAE) and Root Mean Square Error ( RSME) as the evaluation index. The results show that compared to the traditional collaborative filtering recommendation algorithms, MAE of the proposed method increases about 6. 8% and the optimal value reaches 0 . 7513 , and the t-test results also show that the proposed method improves the performance significantly.