协同过滤算法中冷启动问题研究
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摘要
为了解决传统协同过滤算法的冷启动问题,提高算法的推荐质量,本文针对协同过滤算法中的冷启动问题进行研究,提出了两种改进的算法.新用户冷启动:融合用户信息模型的基于用户的协同过滤算法;新项目冷启动:采用层次聚类的基于项目的协同过滤算法.将新算法在网络开源数据集MovieLens上进行实验验证,比较改进算法和传统算法在查全率和查准率上的差异,结果表明改进算法能够有效地提高算法的推荐质量,缓解新用户和新项目的冷启动问题.
In order to solve the cold-start problem of the traditional collaborative filtering algorithm and to improve the performance of recommendation, this study focuses on the cold-start problem and proposes two algorithms. Cold-start problem of new users: user-based collaborative filtering algorithm integrated with user's information model, cold-start problem of new items: item-based collaborative filtering algorithm applying hierarchical clustering. After a series of experiments carried out on public data sets—MovieLens, comparing the difference between the precision and recall value of the improved algorithm and the traditional one, the results show that the new algorithm can effectively alleviate the cold start problem and improve the quality of recommendation.
In order to solve the cold-start problem of the traditional collaborative filtering algorithm and to improve the performance of recommendation, this study focuses on the cold-start problem and proposes two algorithms. Cold-start problem of new users: user-based collaborative filtering algorithm integrated with user's information model, cold-start problem of new items: item-based collaborative filtering algorithm applying hierarchical clustering. After a series of experiments carried out on public data sets—MovieLens, comparing the difference between the precision and recall value of the improved algorithm and the traditional one, the results show that the new algorithm can effectively alleviate the cold start problem and improve the quality of recommendation.
引文
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2 Guo YY,Liu QC.E-commerce personalized recommendation system based on multi-agent.Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery.Yantai,China.2010.1999-2003.
3 Bobadilla J,Ortega F,Hernando A.A collaborative filtering similarity measure based on singularities.Information Processing&Management,2012,48(2):204-217.
4 Hu R,Pu P.Enhancing collaborative filtering systems with personality information.Proceedings of the 5th ACMConference on Recommender Systems.Chicago,IL,USA2011.197-204.
5 Wang JW.A collaborative filtering systems based on personality information.Proceedings of 2015 International Industrial Informatics and Computer Engineering Conference.Xi’an,China.2015.[doi:10.2991/iiicec-15.2015.163]
6 Goldberg D,Nichols D,Oki BM,et al.Using collaborative filtering to weave an information tapestry.Communications of the ACM,1992,35(12):61-70.[doi:10.1145/138859138867]
7申辉繁.协同过滤算法中冷启动问题的研究[硕士学位论文].重庆:重庆大学,2015.
8马卓.协同过滤推荐算法的研究与改进[硕士学位论文].秦皇岛:燕山大学,2015.
9 Xu JW,Yao Y,Tong HH,et al.RaPare:A generic strategy for cold-start rating prediction problem.IEEE Transactions on Knowledge and Data Engineering,2017,29(6):1296-1309.[doi:10.1109/TKDE.2016.2615039]
10 Nguyen VD,Sriboonchitta S,Huynh VN.Using community preference for overcoming sparsity and cold-start problem in collaborative filtering system offering soft ratings Electronic Commerce Research and Applications,2017,26101-108.[doi:10.1016/j.elerap.2017.10.002]
11 Katarya R,Verma OP.Effective collaborative movi recommender system using asymmetric user similarity and matrix factorization.Proceedings of 2016 Internationa Conference on Computing,Communication and Automation Noida,India.2016.71-75.
12乔雨,李玲娟.推荐系统冷启动问题解决策略研究.计算机技术与发展,2018,28(2):83-87.[doi:10.3969/j.issn.1673629X.2018.02.019]
13 http://www.grouplens.org/node/73.
14程淑玉.基于协同过滤算法的个性化推荐系统的研究[硕士学位论文].合肥:合肥工业大学,2010.
15 Breese JS,Heckerman D,Kadie C.Empirical analysis o predictive algorithms for collaborative filtering.Proceeding of the 14th Conference on Uncertainty in Artificia Intelligence.Madison,WI,USA.1998.43-52.
2 Guo YY,Liu QC.E-commerce personalized recommendation system based on multi-agent.Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery.Yantai,China.2010.1999-2003.
3 Bobadilla J,Ortega F,Hernando A.A collaborative filtering similarity measure based on singularities.Information Processing&Management,2012,48(2):204-217.
4 Hu R,Pu P.Enhancing collaborative filtering systems with personality information.Proceedings of the 5th ACMConference on Recommender Systems.Chicago,IL,USA2011.197-204.
5 Wang JW.A collaborative filtering systems based on personality information.Proceedings of 2015 International Industrial Informatics and Computer Engineering Conference.Xi’an,China.2015.[doi:10.2991/iiicec-15.2015.163]
6 Goldberg D,Nichols D,Oki BM,et al.Using collaborative filtering to weave an information tapestry.Communications of the ACM,1992,35(12):61-70.[doi:10.1145/138859138867]
7申辉繁.协同过滤算法中冷启动问题的研究[硕士学位论文].重庆:重庆大学,2015.
8马卓.协同过滤推荐算法的研究与改进[硕士学位论文].秦皇岛:燕山大学,2015.
9 Xu JW,Yao Y,Tong HH,et al.RaPare:A generic strategy for cold-start rating prediction problem.IEEE Transactions on Knowledge and Data Engineering,2017,29(6):1296-1309.[doi:10.1109/TKDE.2016.2615039]
10 Nguyen VD,Sriboonchitta S,Huynh VN.Using community preference for overcoming sparsity and cold-start problem in collaborative filtering system offering soft ratings Electronic Commerce Research and Applications,2017,26101-108.[doi:10.1016/j.elerap.2017.10.002]
11 Katarya R,Verma OP.Effective collaborative movi recommender system using asymmetric user similarity and matrix factorization.Proceedings of 2016 Internationa Conference on Computing,Communication and Automation Noida,India.2016.71-75.
12乔雨,李玲娟.推荐系统冷启动问题解决策略研究.计算机技术与发展,2018,28(2):83-87.[doi:10.3969/j.issn.1673629X.2018.02.019]
13 http://www.grouplens.org/node/73.
14程淑玉.基于协同过滤算法的个性化推荐系统的研究[硕士学位论文].合肥:合肥工业大学,2010.
15 Breese JS,Heckerman D,Kadie C.Empirical analysis o predictive algorithms for collaborative filtering.Proceeding of the 14th Conference on Uncertainty in Artificia Intelligence.Madison,WI,USA.1998.43-52.