Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website
Tóm tắt
In the era of electronic commerce, online customer reviews (OCRs) have become a prevalent and valuable information source for both customers and merchants to make business decisions. This paper proposes an enhanced collaborative filtering approach based on sentiment assessment to discover the potential preferences of customers, and to predict customers’ future requirements for business services or products (collectively referred to as “entities”). Specifically, this approach involves three major steps: aspect-level sentiment assessment, customer preference mining and personalized recommendation. First, the aspect-level sentiment assessment transforms OCRs to a structured aspect-level review vector. Second, customer preference mining uses the vector to extract aspect-level feature words from sentiments and assigns polarity score to each sentiment. Finally, the feature words and sentiment polarity score are used to calculate customer preference and customers’ similarities. Personalized recommendation for services and products are generated according to customer similarity. Experiments are conducted based on the data from one of the most popular electronic commerce websites in China (
www.JD.com
). The results demonstrate that the proposed approach outperforms traditional collaborative filtering approaches in effectively recommending entities to target customers especially in the long term.
Tài liệu tham khảo
Sarwar, B., Karypis, G., Konstan J., & Riedl, J., et al. (2000). Analysis of recommendation algorithms for e-commerce. ACM Conference on Electronic Commerce, 4, 158–167.
Huang, Z., Zeng, D., & Chen, H. (2007). A comparison of collaborative-filtering recommendation algorithms for e-commerce. Intelligent Systems IEEE, 22(5), 68–78.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In International conference on world wide web (Vol. 4, pp. 285–295). ACM.
Falkner, A. A., Felfernig, A., & Haag, A. (2011). Recommendation technologies for configurable products. Ai Magazine, 32(3), 99–108.
Schafer, J. B., Dan, F., Herlocker, J., & Sen, S. (2007). Collaorative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.
Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2012). A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems, 26, 225–238.
Guo, G. (2012). Resolving data sparsity and cold start in recommender systems. In International conference on User Modeling, Adaptation, and Personalization (UMAP), Montreal, Canada (pp. 361–364).
Feng, H., Tian, J., Wang, H. J., & Li, M. (2015). Personalized recommendations based on time-weighted overlapping community detection. Information & Management, 52(7), 789–800.
Qiu, J., Lin, Z., & Li, Y. (2015). Predicting customer purchase behavior in the e-commerce context. Electronic Commerce Research, 15(4), 427–452.
Sohail, S. S., Siddiqui, J., & Ali, R. (2016). Feature extraction and analysis of online reviews for the recommendation of books using opinion mining technique. Perspectives in Science, 8(C), 754–756.
Sarwar, B. M., Karypis, G., Konstan, J., & Riedl, J. (2002). Incremental SVD-based algorithms for highly scaleable recommender systems. Conference on computer and information technology (pp. 345–354).
Lin, S., Zheng, S., & Peng, S. (2015). Data mining model based on the improved BP neural network algorithm. Open Automation & Control Systems Journal, 7(1), 2268–2272.
Jia, W., Zhao, D., Shen, T., Ding, S., & Zhao, Y. (2015). An optimized classification algorithm by BP neural network based on pls and hca. Applied Intelligence, 43(1), 1–16.
Lizhen, L. I. U., Wei, S., Hanshi, W., Chuchu, L., & Jingli, L. (2014). A novel feature-based method for sentiment analysis of Chinese product reviews. China Communications, 11(3), 154–164.
Li, S., Zhou, G., Wang, Z., Lee, S. Y. M., & Wang R. (2011). Imbalanced sentiment classification. In Proceedings of CIKM-2011, pp. 2469–2472.
Tan, S., & Zhang, J. (2008). An empirical study of sentiment analysis for chinese documents. Expert Systems with Applications, 34(4), 2622–2629.
Wan, X. (2008). Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of EMNLP-2008, pp. 553–561.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing, pp. 79–86.
Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), 143–157.
Andreevskaia, A., & Bergler, S. (2008). When specialists and generalists work together: Overcoming domain dependence in sentiment tagging. In Proceedings of ACL-08: HLT, pp. 290–298.
Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53(8), 951–963.
Ma, Y., Chen, G., & Wei, Q. (2017). Finding users preferences from large-scale online reviews for personalized recommendation. Electronic Commerce Research, 17, 1–27.
Wang, X., Zhang, H., & Zheng, X. (2016). Public sentiments analysis based on fuzzy logic for text. International Journal of Software Engineering and Knowledge Engineering, 26(9–10), 1341–1360.
Jiang, D., Luo, X., Xuan, J., & Zheng, X. (2017). Sentiment computing for the news event based on the social media Big Data. IEEE Access, 5, 2373–2382.
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. (1990). Introduction to wordnet: An on-line lexical database. International Journal of Lexicography, 3(4), 235–312.
Esuli, A., & Sebastiani, F. (2006). SentiWordNet: A publicly available lexical resource for opinion mining. In Proceedings of the 5th conference on language resources and evaluation (LREC), Genova, Italy (pp. 417–422).
Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for chinese online social reviews based on topic modeling and hownet lexicon. Knowledge-Based Systems, 37(2), 186–195.
Kim, S. M., & Hovy, E. (2004). Determining the sentiment of opinions. In Proceedings of COLING-04, 20th international conference on computational linguistics, pp. 1367–1373.
Kennedy, A., & Inkpen, D. (2006). Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2), 110–125.
Devitt, A., & Ahmad, K. (2007). Sentiment polarity identification in financial news: A cohesion based approach. In Proceedings of ACL-07, pp. 984–991.
Chen, L., & Wang, F. (2013). Preference-based clustering reviews for augmenting e-commerce recommendation. Knowledge-Based Systems, 50(C), 44–59.
Xu, X., Cheng, X., Tan, S., Liu, Y., & Shen, H. (2013). Aspect-level opinion mining of online customer reviews. Wireless Communication Over Zigbee for Automotive Inclination Measurement China Communications, 10(3), 25–41.
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. SSRN Electronic Journal, 27(4), 293–307.
Yu, J., Zha, Z. J., Wang, M., & Chua, T. S. (2011). Aspect ranking: identifying important product aspects from online consumer reviews. The, meeting of the Association for Computational Linguistics: Human language technologies, proceedings of the conference, 19–24 June, 2011, Portland, Oregon, USA (pp. 1496–1505). DBLP.
Li, Y. M., Wu, C. T., & Lai, C. Y. (2013). A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decision Support Systems, 55(3), 740–752.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (pp. 168–177).
Zhang, W., Xu, H., & Wan, W. (2012). Weakness finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39, 10283–10291.
Decker, R., & Trusov, M. (2010). Estimating aggregate customer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293–307.
Jannach, D., Karakaya, Z., & Gedikli, F. (2012). Accuracy improvements for multi-criteria recommender systems. In Proceedings of the 13th ACM conference on electronic commerce (pp. 674–689).
Yang, Y., & Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. In Proceedings of the fourteenth international conference on machine learning (pp. 412–420).
Gan, M., & Jiang, R. (2013). Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities. Decision Support Systems, 55(3), 811–821.
Kostyra, D. S., Reiner, J., Natter, M., & Klapper, D. (2015). Decomposing the effects of online customer reviews on brand, price, and product attributes. International Journal of Research in Marketing, 33(1), 11–26.