一种面向稀疏数据的比率相似度计算方法
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摘要
针对传统协同过滤方法存在数据稀疏问题,该文提出了一种面向稀疏数据的比率相似度计算方法,该方法在相似度计算过程中仅基于用户全部的显式评分数据,并且不依赖于共同评分项。用户的未评分项目通过相似度计算结果和最近邻的评分数据进行预测,并将预测评分较高的项目推荐给用户,实现个性化推荐。实验在两个公开的数据集上进行,结果表明,在数据稀疏的情况,该方法下仍然能够实现较高的推荐精度。
Aiming at the data sparsity problem in the traditional collaborative filtering method, a ratio similarity calculation method for sparse data is proposed, which is only based on the user′s explicit rating data in the similarity calculation process, and does not depend on co-rated items. The user′s unrated items are predicted by the similarity calculation results and the nearest neighbor′s rating data, and the items with the higher predicted ratings are recommended to the user to implement personalized recommendation. The experiment was carried out on two public datasets. The experimental results show that in the case of sparse data, the method can still maintain a high recommendation accuracy.
Aiming at the data sparsity problem in the traditional collaborative filtering method, a ratio similarity calculation method for sparse data is proposed, which is only based on the user′s explicit rating data in the similarity calculation process, and does not depend on co-rated items. The user′s unrated items are predicted by the similarity calculation results and the nearest neighbor′s rating data, and the items with the higher predicted ratings are recommended to the user to implement personalized recommendation. The experiment was carried out on two public datasets. The experimental results show that in the case of sparse data, the method can still maintain a high recommendation accuracy.
引文
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[15] BOBADILLA J,ORTEGA F,HERNANDO A.A collaborative filtering similarity measure based on singularities[J].Information Processing & Management,2012,48(2):204-217.
[16] ANAND D,BHARADWAJ K K.Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities[J].Expert Systems with Applications,2011,38(5):5101-5109.
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[2] AI-SHAMRI,MOHAMMAD Y H.User profiling approaches for demographic recommender systems[J].Knowledge-Based Systems,2016,100:175-187.
[3] KIM H N,HA I,LEE K S,et al.Collaborative user modeling for enhanced content filtering in recommender systems[J].Decision Support Systems,2011,51(4):772-781.
[4] SILVA E Q D,CAMILO-JUNIOR C G,PASCOAL L M L,et al.An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering[J].Expert Systems with Applications,2016,53:204-218.
[5] BAARRAGáNS-MARTíNEZ A B,COSTA-MONTENEGRO E,BURGUILLO J C,et al.A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition[J].Information Sciences,2010,180(22):4290-4311.
[6] LIU H,HU Z,MIAN A,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge-Based Systems,2014,56:156-166.
[7] AGHDAM M H,ANALOUI M,KABIRI P.Collaborative filtering using non-negative matrix factorisation[J].Journal of Information Science,2016,43(4),1-13.
[8] POLATO M,AIOLLI F.Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation[J].Neurocomputing,2017:1-10.
[9] HERNANDO A,ORTEGA F.A probabilistic model for recommending to new cold-start non-registered users[J].Information Sciences,2017,376(C):216-232.
[10] CAMACHO L A G,ALVES-SOUZA S N.Social network data to alleviate cold-start in recommender system:A systematic review[J].Information Processing & Management,2018,54(4):529-544.
[11] 程伟杰,印桂生,董宇欣,等.一种使用全部评分提高推荐精度的方法[J].西北工业大学学报,2017,35(5):928-934.
[12] GUO G,QIU H,TAN Z,et al.Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems[J].Knowledge-Based Systems,2017,138:202-207.
[13] SURYAKANT MAHARA T.A new similarity measure based on mean measure of divergence for collaborative filtering in sparse environment[J].Procedia Computer Science,2016,89:450-456.
[14] AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.
[15] BOBADILLA J,ORTEGA F,HERNANDO A.A collaborative filtering similarity measure based on singularities[J].Information Processing & Management,2012,48(2):204-217.
[16] ANAND D,BHARADWAJ K K.Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities[J].Expert Systems with Applications,2011,38(5):5101-5109.
[17] SUN S B,ZHANG Z H,DONG X L,et al.Integrating Triangle and Jaccard similarities for recommendation[J].PlOSl ONE,2017,12(8):e0183570.