Personalized Book Recommendation Based on User Preferences and Commodity Features
Hou Yinxiu, Li Weiqing, Wang Weijun(), Zhang Tingting
School of Information Management, Central China Normal University, Wuhan 430079, China Key Laboratory of Adolescent Cyber Psychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
[Objective] This paper identifies the fine-grained preferences of online bookstore users, aiming to optimize the personalized book recommendation service. [Methods] First, we conducted sentiment analysis of the book features through readers’ comments, which indicated their preferences. Then, we calculated the books’ sentiment scores based on the readers’ comments. Finally, the user preferences matrix and the sentiment scores matrix were matched to personalize the book recommendation. [Results] We retrieved the needed data from Amazon’s book comments, and then conducted an experiment to compare the results of our new method with those of the traditional collaborative filtering methods. We found that the proposed method improved the precision, recall and coverage by 0.030, 0.097, 0.2812. [Limitations] We did not consider the impacts of time on user’s preferences, and the feature types might not be comprehensive due to the limited number and quality of Amazon’s book comments. [Conclusions] The proposed method improves the performance of personalized book recommendation service.
侯银秀, 李伟卿, 王伟军, 张婷婷. 基于用户偏好与商品属性情感匹配的图书个性化推荐研究*[J]. 数据分析与知识发现, 2017, 1(8): 9-17.
Hou Yinxiu,Li Weiqing,Wang Weijun,Zhang Tingting. Personalized Book Recommendation Based on User Preferences and Commodity Features. Data Analysis and Knowledge Discovery, 2017, 1(8): 9-17.
(Liu Jianguo, Zhou Tao, Wang Binghong.Research Progress of Personalized Recommendation System[J]. Progress in Natural Science, 2009, 19(1): 1-15.)
doi: 10.3321/j.issn:1002-008X.2009.01.001
[2]
Xu Y, Yin J.Collaborative Recommendation with User Generated Content[J]. Engineering Applications of Artificial Intelligence, 2015, 45: 281-294.
doi: 10.1016/j.engappai.2015.07.012
[3]
Hu N, Koh N S, Reddy S K.Ratings Lead You to the Product, Reviews Help You Clinch It? The Mediating Role of Online Review Sentiments on Product Sales[J]. Decision Support Systems, 2014, 57(1): 42-53.
doi: 10.1016/j.dss.2013.07.009
[4]
Sohail S S, Siddiqui J, Ali R.Book Recommendation System Using Opinion Mining Technique[C]////Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics. IEEE, 2013: 1609-1614.
[5]
Diao Q, Qiu M, Wu C Y, et al.Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS)[C]////Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2014: 193-202.
[6]
Liu Q, Liu K, Wang Y, et al.User-Oriented Real-Time Preference Obtaining Screen Visual Hotspots Recognition Based on the Research of Eye Movement Experiments[C]//// Proceedings of the 1st International Conference on Information Acquisition and Knowledge Services.2014, 39: 181-185.
[7]
刘凯. 基于屏幕视觉热区的用户偏好提取及个性化推荐[M]. 北京: 科学出版社, 2016.
[7]
(Liu Kai.User Preference Extraction and Personalized Recommendation Based on Screen Visual Hotspot[M]. Beijing: Science Press, 2016.)
(Li Shuqing, Xu Xia, Xu Minjia.The Measures of Books’ Recommending Quality and Personalized Book Recommendation Service Based on Bipartite Network of Readers and Books’ Lending Relationship[J]. Journal of Library Science in China, 2013, 39(3): 83-95.)
doi: 10.3969/j.issn.1001-8867.2013.03.021
(Lin Yaojin, Hu Xuegang, Li Huizong.Collaborative Filtering Recommendation Algorithm Based on User Group Influence[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(3): 299-305.)
doi: 10.3772/j.issn.1000-0135.2013.03.009
[10]
Liao C L, Lee S J.A Clustering Based Approach to Improving the Efficiency of Collaborative Filtering Recommendation[J]. Electronic Commerce Research & Applications, 2016, 18: 1-9.
doi: 10.1016/j.elerap.2016.05.001
[11]
Najafabadi M K, Mahrin M N, Chuprat S, et al.Improving the Accuracy of Collaborative Filtering Recommendations Using Clustering and Association Rules Mining on Implicit Data[J]. Computers in Human Behavior, 2017, 67: 113-128.
doi: 10.1016/j.chb.2016.11.010
(Wang Weijun, Song Meiqing.A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining User’ Preferences[J]. New Technology of Library and Information Service, 2014(6): 25-32.)
[13]
Qiu L, Gao S, Cheng W, et al.Aspect-based Latent Factor Model by Integrating Ratings and Reviews for Recommender System[J]. Knowledge-Based Systems, 2016, 110: 233-243.
doi: 10.1016/j.knosys.2016.07.033
[14]
Li Y M, Wu C T, Lai C Y.A Social Recommender Mechanism for E-commerce: Combining Similarity, Trust, and Relationship[J]. Decision Support Systems, 2013, 55(3): 740-752.
doi: 10.1016/j.dss.2013.02.009
(Liu Kai, Wang Weijun, Huang Yinghui, et al.Exploration on Personalized Recommendation System Theory: Evolution from System-centric to User-centric[J]. Information Studies: Theory & Application, 2016, 39(3): 52-56.)
doi: 10.16353/j.cnki.1000-7490.2016.03.011
[16]
丁雪. 认知视角下网络用户偏好复合模型的构建与验证[D]. 武汉: 华中师范大学, 2016.
[16]
(Ding Xue.A Composite User Preference Model: An Experimental Model from a Cognitive Perspective[D]. Wuhan: Central China Normal University, 2016.)
[17]
Mcauley J, Leskovec J.Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text[C]//// Proceedings of the 7th ACM Conference on Recommender Systems. 2013: 165-172.
(Li Shi, Ye Qiang, Li Yijun, et al.Mining Features of Products from Chinese Customer Online Reviews[J]. Journal of Management Sciences in China, 2009, 12(2): 142-151.)
doi: 10.3321/j.issn:1007-9807.2009.02.015
[19]
He T, Hao R, Qi H, et al.Mining Feature-Opinion from Reviews Based on Dependency Parsing[J]. International Journal of Software Engineering & Knowledge Engineering, 2016, 26(10): 1581-1591.
doi: 10.1142/S0218194016710029
[20]
Lopez-Arevalo I, Sosa-Sosa V J, Rojas-Lopez F, et al. Improving Selection of Synsets from WordNet for Domain-specific Word Sense Disambiguation[J]. Computer Speech & Language, 2017, 41(10): 128-145.
doi: 10.1016/j.csl.2016.06.003
[21]
Hung C, Chen S J.Word Sense Disambiguation Based Sentiment Lexicons for Sentiment Classification[J]. Knowledge-Based Systems, 2016, 110(5): 224-232.
doi: 10.1016/j.knosys.2016.07.030
