[Objective] This paper proposes a review-based user modeling method, aiming to improve the personalized information pushing services. [Methods] Firstly, we identified product feature-specific terms from reviews with the help of pre-trained word embedding model. Then, we built a term-specific graph based on semantic correlation among feature-specific words. Finally, we used the TextRank algorithm to compute user’s interest in product features, and model their preferences for products. [Results] User model generated by our new algorithm was consistent with the manually created ones (with nearly 90% semantic correlation). Our F1-score was 0.55, better than those of the classic TF-based word bag models. [Limitations] More manually labeled data and research is needed to improve the domain-specific analysis. [Conclusions] The proposed model helps us better analyze online reviews and develop new application for recommendation system.
聂卉. 结合词向量和词图算法的用户兴趣建模研究 *[J]. 数据分析与知识发现, 2019, 3(12): 30-40.
Hui Nie. Modeling Users with Word Vector and Term-Graph Algorithm. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2019.0494.
姜霖, 张麒麟 . 基于评论情感分析的个性化推荐策略研究-以豆瓣影评为例[J]. 情报理论与实践, 2017,40(8):99-104. ( Jiang Lin, Zhang Qilin . Research on Personalized Recommendation Strategy Based on Sentimental Analysis of the Reviews[J]. Information Studies: Theory & Application, 2017,40(8):99-104.)
[2]
Chen L, Chen G, Wang F . Recommender Systems Based on User Reviews: The State of the Art[J]. User Modeling and User-Adapted Interaction, 2015,25(2):99-154.
[3]
宁建飞, 刘降珍 . 融合Word2vec与TextRank的关键词抽取研究[J]. 现代图书情报技术, 2016(6):20-27. ( Ning Jianfei, Liu Jiangzhen . Using Word2vec with TextRank to Extract Keywords[J]. New Technology of Library and Information Service, 2016(6):20-27.)
[4]
徐文海, 温有奎 . 一种基于TFIDF方法的中文关键词抽取算法[J]. 情报理论与实践, 2008,31(2):298-302. ( Xu Wenhai, Wen Youkui . An TFIDF_based Algorithm for Chinese Keywords Extraction[J]. Information Studies: Theory & Application, 2008,31(2):298-302.)
[5]
刘俊, 邹东升, 邢欣来 , 等. 基于主题特征的关键词抽取[J]. 计算机应用研究, 2012,29(11):4224-4227. ( Liu Jun, Zou Dongsheng, Xing Xinlai , et al. Keyphrase Extraction Based on Topic Feature[J]. Application Research of Computers, 2012,29(11):4224-4227.)
[6]
Mihalcea R, Tarau P . TextRank: Bringing Order into Texts [C]//Proceedings of Empirical Methods in Natural Language Processing, Barcelona, Spain. 2004: 404-411.
[7]
夏天 . 词语位置加权TextRank的关键词抽取研究[J]. 现代图书情报技术, 2013(9):30-34. ( Xia Tian . Study on Keyword Extraction Using Word Position Weighted TextRank[J]. New Technology of Library and Information Service, 2013(9):30-34.)
[8]
谢玮, 沈一, 马永征 . 基于图计算的论文审稿自动推荐系统[J]. 计算机应用研究, 2016,33(3):798-801. ( Xie Wei, Shen Yi, Ma Yongzheng . Recommendation System for Paper Reviewing Based on Graph Computing[J]. Application Research of Computers, 2016,33(3):798-801.)
[9]
顾益军, 夏天 . 融合LDA与TextRank的关键词抽取研究[J]. 现代图书情报技术, 2014(7/8):41-47. ( Gu Yijun, Xia Tian . Study on Keyword Extraction with LDA and TextRank Combination[J]. New Technology of Library and Information Service, 2014(7/8):41-47.)
[10]
夏天 . 词向量聚类加权TextRank的关键词抽取[J]. 数据分析与知识发现, 2017,1(2):28-34. ( Xia Tian . Extracting Keywords with Modified TextRank Model[J]. Data Analysis and Knowledge Discovery, 2017,1(2):28-34.)
[11]
Esparza S G, O’Mahony M P, Smyth B . Effective Product Recommendation Using the Real-Time Web [C]//Proceedings of the 30th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK. Springer, 2010: 5-18.
[12]
Zhang W, Ding G, Chen L , et al. Generating Virtual Ratings from Chinese Reviews to Augment Online Recommendations [J]. ACM Transactions on Intelligent Systems and Technology, 2013, 4(1): Article No. 9.
[13]
Musat C C, Liang Y, Faltings B . Recommendation Using Textual Opinions [C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China. AAAI Press, 2013: 2684-2690.
[14]
McAuley J, Leskovec J . Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text [C] //Proceedings of the 7th ACM International Conference on Recommender Systems, Hong Kong, China. New York, USA: ACM, 2013: 165-172.
[15]
Liu H, He J, Wang T , et al. Combining User Preferences and User Opinions for Accurate Recommendation[J]. Electronic Commerce Research and Applications, 2013,12(1):14-23.
[16]
Chen L, Wang F . Preference-based Clustering Reviews for Augmenting E-commerce Recommendation[J]. Knowledge-Based Systems, 2013,50:44-59.
[17]
Chen L, Wang F . Explaining Recommendations Based on Feature Sentiments in Product Reviews [C]// Proceedings of the 22nd International Conference on Intelligent User Interfaces, Limasso, Cyprus. New York, USA: ACM, 2017: 17-28.
[18]
王伟, 王洪伟 . 面向竞争力的特征比较网络: 情感分析方法[J]. 管理科学学报, 2016,19(9):109-126. ( Wang Wei, Wang Hongwei . Comparative Network for Product Competition in Feature-levels Through Sentiment Analysis[J]. Journal of Management Sciences in China, 2016,19(9):109-126.)
[19]
Hong Y, Lu J, Yao J , et al. What Reviews are Satisfactory: Novel Features for Automatic Helpfulness Voting [C] //Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, Oregon, USA. New York, USA: ACM, 2012: 495-504.
[20]
Chinese Word Vectors: 目前最全的中文预训练词向量集合[EB/OL]. [ 2018- 10- 20]. http://www.mingriqingbao.com/web/detail/forword/P/12571. ( Chinese Word Vectors: The Most Complete Set of Chinese Pre-trained Word Vectors [EB/OL]. [ 2018- 10- 20]. http://www.mingriqingbao.com/web/detail/forword/P/12571
[21]
聂卉, 杜嘉忠 . 依存句法模板下的商品特征标签抽取研究[J]. 现代图书情报技术, 2014(12):44-50. ( Nie Hui, Du Jiazhong . Using Dependency Parsing Pattern to Extract Product Feature Tags[J]. New Technology of Library and Information Service, 2014(12):44-50.)