基于项目类型的群组推荐方法
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摘要
近年来,群组推荐吸引了大量研究人员的关注。针对群组推荐中融合策略的不足而导致群组推荐结果准确率低的问题,提出了一种新的基于项目类型的方法来改进偏好融合策略,以此提高推荐结果的准确性。通过引入项目类型占比因子并计算群组类型偏好和用户类型偏好之间的类型相似性,同时提出了评分融合公式来预测群组的最终项目分数,从而改进偏好融合策略。最后,在Movielens数据集上进行实验并将本文方法与几种经典的群组推荐方法进行比较。结果表明,本文方法比传统基线方法具有更高的准确率。
Group recommendation has attracted significant research attention in recent years. In view of the problem of insufficiency of fusion strategy in group recommendation which cause the low accuracy, we propose an novel method to improve preference fusion strategy to increaandse the accuracy. We introduce the concept of item type proportion factor and calculate the type similarity between group type preference and user type preference. Meanwhile we design a score fusion formula to predict item score for the group. Finally, we carry out experiments and compare our method with several classic group recommendation methods using Movielens dataset. The results show that our method achieves higher recommendation accuracy than all the baseline methods.
Group recommendation has attracted significant research attention in recent years. In view of the problem of insufficiency of fusion strategy in group recommendation which cause the low accuracy, we propose an novel method to improve preference fusion strategy to increaandse the accuracy. We introduce the concept of item type proportion factor and calculate the type similarity between group type preference and user type preference. Meanwhile we design a score fusion formula to predict item score for the group. Finally, we carry out experiments and compare our method with several classic group recommendation methods using Movielens dataset. The results show that our method achieves higher recommendation accuracy than all the baseline methods.
引文
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[2] RESNICK P,VARIAN H R.Recommender systems[J].Communications of the Acm,1997,40(3):56-58.
[3] 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362.
[4] GARCIA I,SEBASTIA L,ONAINDIA E.On the design of individual and group recommender systems for tourism[J].Expert Systems with Applications,2011,38:7683-7692.
[5] 张玉洁,杜雨露,孟祥武.组推荐系统及其应用研究[J].计算机学报,2016,39(4):745-764.
[6] JAMESON A.More than the sum of its members:Challenges for group recommender systems[C].Working Conference on Advanced Visual Interfaces,ACM,2004.
[7] JAMESON A,SMYTH B.Recommendation to Groups[M].The Adaptive Web,2007.
[8] LARA Q S,JUAN A,BELEN D A.Personality and social trust in group recommendations[C].IEEE International Conference on Tools with Artificial Intelligence.IEEE Computer Society,2010.
[9] 陈琦,吕杰,张世超.一个解决协同过滤推荐系统相关问题的新算法[J].电子测量技术,2016,39(5):66-69.
[10] RICCI F,ROKACH L,SHAPIRA B,et al.Recommender Systems Handbook[M].Springer US,2011.
[11] ZHANG Y J,DU Y L,MENG X W.Research on group recommender systems and their applications[J].Chinese Journal of Computers,2016,39(4):745-764.
[12] 胡川,孟祥武,张玉洁,等.一种改进的偏好融合群组推荐方法[J].软件学报,2018,29(10):3164-3183.
[13] MASTHOFF J.Group modeling:Selecting a sequence of television items to suit a group of viewer[C]].User Model,User-Adapt,Interact,2004.
[14] HARPER F M,KONSTAN J A.The MovieLens Datasets:History and Context[M].ACM,2015.
[15] YUAN Q,CONG G,LIN C Y.Com:a generative model for group recommendation[J].2014(8):163-172.
[16] BALTRUNAS L,MAKCINSKAS T,RICCI F.Group recommendations with rank aggregation and collaborative filtering[C].ACM Conference on Recommender Systems,2010.
[17] AMER-YAHIA S,ROY S B,CHAWLAT A,et al.Group recommendation:Semantics and efficiency[J].Proceedings of the VLDB Endowment,2009,2(1):754-765.
[18] TOON D P,SIMON D,LUC M.Comparison of group recommendation algorithms[J].Multimedia Tools and Applications,2014,72(3):2497-2541.