计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 147-154.doi: 10.11896/jsjkx.200300072
武建新, 张志鸿
WU Jian-xin, ZHANG Zhi-hong
摘要： 协同过滤算法是推荐系统中使用最广泛的算法,其核心是利用某兴趣爱好相似的群体来为用户推荐感兴趣的信息。传统的协同过滤算法利用用户-项目评分矩阵计算相似度,通过相似度寻找用户的相似群体来进行推荐,但是由于其评分矩阵的稀疏性问题,对相似度的计算不够准确,这间接导致推荐系统的质量下降。为了缓解数据稀疏性对相似度计算的影响并提高推荐质量,提出了一种融合用户评分与用户显隐兴趣的相似度计算方法。该方法首先利用用户-项目评分矩阵计算用户评分相似度;然后根据用户基本属性与用户-项目评分矩阵得出项目隐性属性;之后综合项目类别属性、项目隐性属性、用户-项目评分矩阵和用户评分时间,得到用户显隐兴趣相似度;最后融合用户评分相似度和用户显隐兴趣相似度得到用户相似度,并以此相似度寻找用户的相似群体以进行推荐。在数据集Movielens上的实验结果表明,相比传统算法中仅使用单一的评分矩阵来计算相似度,提出的新相似度计算方法不仅能够更加准确地寻找到用户的相似群体,而且还能够提供更好的推荐质量。
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| [1]LI M M,WANG L C.A Survey on Personalized News Recom-mendation Technology[J]. IEEE Access,2019,7(99):145861-145879. [2]JALILI M,AHMADIAN S,IZADI M,et al.Evaluating Collaborative Filtering Recommender Algorithms:A Survey[J].IEEE Access,2018,6:74003-74024. [3]NATARAJAN S,VAIRAVASUNDARAM S,NATARAJANS,et al.Resolving data sparsity and cold start problem in colla-borative filtering recommender system using Linked Open Data[J].Expert System with Applications,2020,149:113248. [4]WU C H,WU F Z,QI T,et al.Reviews Meet Graphs.Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network[C]//EMNLP/IJCNLP.2019:4883-4892. [5]SHI W C,WANG L J,QIN J W.User Embedding for RatingPrediction in SVD++-Based Collaborative Filtering[J].Symmetry,2020,12:121. [6]GUO X,ZHU J H.Deep Neural Network RecommendationModel Based on User Vectorization Representation and Attention Mechanism[J].Computer Science,2019,46(8):111-115. [7]LIU H F,HU Z,AHMAD U M,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge Based System,2014,56(Jan.):156-166. [8]TAN Z H,HE L L.An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle[J].IEEE Access,2017,5:27211-27228. [9]JIN Q B,ZHANG Y,CAI W,et al.A New Similarity Computing Model of Collaborative Filtering[J].IEEE Access,2020,8:17594-17604. [10]WANG Y C,LIU Z.Collaborative Filtering Algorithm Based on User's Preference for Items and Attribut [J].Computer Science,2018,45(S2):422-426. [11]LI Z L,HUANG M X,ZHANG Y.A Collaborative Filtering Al-gorithm of Calculating Similarity Based on Item Rating and Attributes[C]//Web Iinformation Systems and Applications.2017:215-218. [12]WEI H J,DAI D H.Collaboration Filtering RecommendationAlgorithm Based on Ratings Difference and Interest Similarity[J].Computer Science,2018,45(S1):411-414,435. [13]ZHANG L,ZHANG Z J,HE J F,et al.UR:A User-Based Collaborative Filtering Recommendation System Based on Trust Mechanism and Time Weighting[C]//2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS).2020:69-76. |
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