采用评论挖掘修正用户评分的改进协同过滤算法
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摘要
针对当前电子商务网站用户评分过于集中而区分度不明显,以及整数评分可信度不高导致协同过滤推荐效果较差的问题,提出一种改进协同过滤算法.利用改进的词性路径模板算法挖掘评论中包含的产品特征和情感词,分析并建立评论特征偏好向量;依据评论特征偏好向量计算评论中包含的情感态度,利用用户评论中包含的情感态度对评分进行修正,使得修正后的评分更接近于用户的真实评分意愿;利用修正后的评分计算评分相似度,与偏好相似度结合产生推荐.实验结果表明,该算法有效地增加了评分区分度与可信度,提高了最近邻居的质量,从而提高了推荐结果的准确度.
An improved collaborative filtering algorithm was proposed aiming at the problems that user ratings for current e-commerce websites are too concentrated, the distinguishing degree is not obvious, and the credibility of the integer rating is not very well. Firstly, the improved part-of-speech path template algorithm was used to mine the product features and sentiment words contained in user's reviews, and then the preference vector of the review was analyzed and established. Secondly, the emotional attitudes included in the review were calculated according to the review's preference vector, and the emotional attitudes were used to revise the user's rating, so that the revised rating was closer to the user's true score willingness. Finally, the revised rating similarity and the preference similarity were combined to produce recommendations. The experimental results show that the proposed algorithm can effectively increase the classification and credibility of user's ratings, and thus improves the quality of the nearest neighbors, as well as the accuracy of the recommended results.
An improved collaborative filtering algorithm was proposed aiming at the problems that user ratings for current e-commerce websites are too concentrated, the distinguishing degree is not obvious, and the credibility of the integer rating is not very well. Firstly, the improved part-of-speech path template algorithm was used to mine the product features and sentiment words contained in user's reviews, and then the preference vector of the review was analyzed and established. Secondly, the emotional attitudes included in the review were calculated according to the review's preference vector, and the emotional attitudes were used to revise the user's rating, so that the revised rating was closer to the user's true score willingness. Finally, the revised rating similarity and the preference similarity were combined to produce recommendations. The experimental results show that the proposed algorithm can effectively increase the classification and credibility of user's ratings, and thus improves the quality of the nearest neighbors, as well as the accuracy of the recommended results.
引文
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[20]PANG B,LEE L.Opinion mining and sentiment analysis[J].Foundations and Trends in Information Retrieval,2008,2(1-2):1-135.
[21]朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.ZHU Yu-xiao,LU Lin-yuan.Evaluation metrics for recommender systems[J].Journal of University of Electronic Science and Technology of China,2012,41(2):163-175.
[2]TAN Y,ZHANG Y,ZHANG M,et al.A unified framework for emotional elements extraction based on finite state matching machine[M]//Natural Language Processing and Chinese Computing.Berlin Heidelberg:Springer,2013:60-71.
[3]ZHANG Y,LAI G,ZHANG M,et al.Explicit factor models for explainable recommendation based on phrase-level sentiment analysis[C]//Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval.Gold Coast:ACM,2014:83-92.
[4]KIRAN M V K,VINODHINI R E,ARCHANAA R,et al.User specific product recommendation and rating system by performing sentiment analysis on product reviews[C]//4th International Conference on Advanced Computing and Communication Systems(ICACCS).Coimbatore:IEEE,2017:1-5.
[5]CHEN G,CHEN L.Augmenting service recommender systems by incorporating contextual opinions from user reviews[J].User Modeling and User-Adapted Interaction,2015,25(3):295-329.
[6]XU X,DUTTA K,DATTA A.Functionality-based mobile app recommendation by identifying aspects from user reviews[J].Journal of the Association for Information Science and Technology,2018,69(2):242-255.
[7]RESCHKE K,VOGEL A,JURAFSKY D.Generating recommendation dialogs by extracting information from user reviews[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics.Sofia:ACL,2013:499-504.
[8]GANU G,MARIAN A,ELHADAD N.Ursa-User review structure analysis:understanding online reviewing trends[R/OL].Rutgers DCS Technical Report,No.668,April 2010.http://spidr-ursa.rutgers.edu/resources/TR_LRE.pdf.
[9]GANU G,KAKODKAR Y,MARIAN A L.Improving the quality of predictions using textual information in online user reviews[J].Information Systems,2013,38(1):1-15.
[10]MCAULEY J,LESKOVEC J.Hidden factors and hidden topics:understanding rating dimensions with review text[C]//Proceedings of the 7th ACMconference on Recommender systems.New York:ACM,2013:165-172.
[11]DEHKORDI Y H,THOMO A,GANTI S.Incorporating user reviews as implicit feedback for improving recommender systems[C]//The 4th IEEE International Conference on Big Data and Cloud Computing.Sydney:IEEE,2014:455-462.
[12]张付志,刘赛,李忠华,等.融合用户评论和环境信息的协同过滤推荐算法[J].小型微型计算机系统,2014,35(2):228-232.ZHANG Fu-zhi,LIU Sai,LI Zhong-hua,et al.Collaborative filtering recommendation algorithm incorporating user's reviews and contextua information[J].Journal of Chinese Computer Systems,2014,35(2):228-232.
[13]谭云志,张敏,刘奕群,等.基于用户评分和评论信息的协同推荐框架[J].模式识别与人工智能,2016,29(4):359-366.TAN Yun-zhi,ZHANG Min,LIU Yi-qun,et al.Collaborative recommendation framework based on ratings and textual reviews[J].Pattern Recognition and Artificial Intelligence,2016,29(4):359-366.
[14]李伟霖,王成良,文俊浩.基于评论与评分的协同过滤算法[J].计算机应用研究,2017,34(2):361-364.LI Wei-lin,WANG Cheng-liang,WEN Jun-hao.Collaborative filtering recommendation algorithm based on reviews and ratings[J].Application Research of Computers,2017,34(2):361-364.
[15]王伟军,宋艳秋,张婷婷,等.基于话语标记理论的在线商品评分修正方法[J].情报学报,2016,35(4):358-368.WANG Wei-jun,SONG Yan-qiu,ZHANG Ting-ting.A correction method for score deviation of online product reviews based on discourse markers theory[J].Journal of The China Society for Scientific and Technical Information,2016,35(4):358-368.
[16]郑修猛,陈福才,黄瑞阳,等.面向协同推荐的评论文本情感打分机制研究[J].信息工程大学学报,2017,18(4):464-469.ZHENG Xiu-meng,CHEN Fu-cai,HUANG Ruiyang,et al.Sentiment rating mechanism research on review text for CF recommendation systems[J].Journal of Information Engineering University,2017,18(4):464-469.
[17]YANG H,SUH Y.Sentiment analysis of online customer reviews for product recommendation:comparison with traditional CF-based recommendation[C]//The Korea Society of Management Information Systems.Seoul:KMIS,2015:801-805.
[18]WU Y,ZHANG Q,HUANG X,et al.Phrase dependency parsing for opinion mining[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language.Singapore:EMNLP,2009:1533-1541.
[19]扈中凯,郑小林,吴亚峰,等.基于用户评论挖掘的产品推荐算法[J].浙江大学学报:工学版,2013,47(8):1475-1485.HU Zhong-kai,ZHENG Xiao-lin,WU Ya-feng,et al.Product recommendation algorithm based on users'reviews mining[J].Journal of Zhejiang University:Engineering Science,2013,47(8):1475-1485.
[20]PANG B,LEE L.Opinion mining and sentiment analysis[J].Foundations and Trends in Information Retrieval,2008,2(1-2):1-135.
[21]朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.ZHU Yu-xiao,LU Lin-yuan.Evaluation metrics for recommender systems[J].Journal of University of Electronic Science and Technology of China,2012,41(2):163-175.