Personalized Ranking of Online Reviews Based on Consumer Preferences in Product Features

References

• Ahmad, S.N., and Laroche, M. How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. International Journal of Electronic Commerce, 20, 1 (2015), 76–111.
   Web of Science ®Google Scholar
• Baek, H., Ahn, J., and Choi, Y. Helpfulness of online consumer reviews: readers’ objectives and review cues. International Journal of Electronic Commerce, 17, 2 (2012), 99–126.
   Web of Science ®Google Scholar
• Banerjee, S., Bhattacharyya, S., and Bose, I. Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96(2017), 17–26.
   Web of Science ®Google Scholar
• Bansal, M.S., and Fernández-Baca, D. Computing distances between partial rankings. Information Processing Letters, 109, 4 (2009), 238–241.
   Web of Science ®Google Scholar
• Berezina, K., Bilgihan, A., Cobanoglu, C., and Okumus, F. Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25, 1 (2016), 1–24.
   Web of Science ®Google Scholar
• Berger, J., Sorensen, A.T., and Rasmussen, S.J. Positive effects of negative publicity: when negative reviews increase sales. Marketing Science, 29(2010), 815–827.
   Web of Science ®Google Scholar
• Blei, D.M., Griffiths, T.L., and Jordan, M.I. The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57(2010), 1–30.
   Web of Science ®Google Scholar
• Blei, D.M., and Lafferty, J.D. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, PA: Association for Computing Machinery, 2006, pp. 113–120.
   Google Scholar
• Blom, J. Personalization: A taxonomy. In CHI ‘00 Extended Abstracts on Human Factors in Computing Systems. The Hague, the Netherlands: Association for Computing Machinery, 2000, pp. 313–314.
   Google Scholar
• Castleman, B., Schwartz, S., and Baum, S. Decision Making for Student Success. New York: Routledge, 2015.
   Google Scholar
• Chen, C.C., and Tseng, Y.-D. Quality evaluation of product reviews using an information quality framework. Decision Support Systems, 50, 4 (2011), 755–768.
   Web of Science ®Google Scholar
• Cheung, C.M.K., and Lee, M.K.O. What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53, 1 (2012), 218–225.
   Web of Science ®Google Scholar
• Chu, W.J., and Roh, M.J. Exploring the role of preference heterogeneity and causal attribution in online ratings dynamics. Asia Marketing Journal, 15, 4 (2014), 61–101.
   Google Scholar
• Chua, A.Y.K., and Banerjee, S. Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Computers in Human Behavior, 54(2016), 547–554.
   Web of Science ®Google Scholar
• Coussement, K., and Antioco, M. Managing information overload: The case of online product review categorization. In Marketing Dynamism & Sustainability: Things Change, Things Stay the Same …. Cham, Switzerland: Springer International, 2015, p. 548.
   Google Scholar
• Delone, W., and McLean, E. Information systems success: The quest for the dependent variable. Information Systems Research, 3(1992), 60–95.
   Web of Science ®Google Scholar
• Delone, W.H., and McLean, E.R. The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19, 4 (2003), 9–30.
   Web of Science ®Google Scholar
• Díaz, A., and Gervás, P. User-model based personalized summarization. Information Processing & Management, 43, 6 (2007), 1715–1734.
   Google Scholar
• Fan, H., and Poole, M.S. What is personalization? Perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce, 16, 3–4 (2006), 179–202.
   Web of Science ®Google Scholar
• Felbermayr, A., and Nanopoulos, A. The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36(2016), 60–76.
   Google Scholar
• Forman, C., Ghose, A., and Wiesenfeld, B. Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19, 3 (2008), 291–313.
   Web of Science ®Google Scholar
• Franek, J., and Kresta, A. Judgment scales and consistency measure in AHP. Procedia Economics and Finance, 12(2014), 164–173.
   Google Scholar
• Furner, C.P., and Zinko, R.A. The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: An empirical investigation. Electronic Markets, 27, 3 (2017), 211–224.
   Web of Science ®Google Scholar
• Ghose, A., and Ipeirotis, P.G., Designing novel review ranking systems: Predicting the usefulness and impact of reviews. In Proceedings of the ninth international conference on Electronic commerce, Minneapolis, MN: Association for Computing Machinery, 2007, pp. 303–310.
   Google Scholar
• Ghose, A., and Ipeirotis, P.G. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23, 10 (2011), 1498–1512.
   Web of Science ®Google Scholar
• He, R., and McAuley, J. VBPR: Visual Bayesian personalized ranking from implicit feedback. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ: AAAI Press, 2016, pp. 144–150.
   Google Scholar
• Hevner, A.R., March, S.T., Park, J., and Ram, S. Design science in information systems research. MIS Quarterly, 28, 1 (2004), 75–105.
   Web of Science ®Google Scholar
• Hong, H., Xu, D., Wang, G.A., and Fan, W. Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102 (2017), 1–11.
   Google Scholar
• Hsieh, J.-K., and Li, Y.-J. Will you ever trust the review website again? The importance of source credibility. International Journal of Electronic Commerce, 24, 2 (2020), 255–275.
   Web of Science ®Google Scholar
• Hu, H., and Krishen, A.S. When is enough, enough? Investigating product reviews and information overload from a consumer empowerment perspective. Journal of Business Research, 100 (2019), 27–37.
   Google Scholar
• Hu, N., Koh, N.S., and Reddy, S.K. Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57(2014), 42–53.
   Google Scholar
• Huang, A.H., Chen, K., Yen, D.C., and Tran, T.P. A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48(2015), 17–27.
   Web of Science ®Google Scholar
• Jin, Z., Zhangwen, W., and Naichen, N. Helping consumers to overcome information overload with a diversified online review subset. Frontiers of Business Research in China, 13, 1 (2019), 15.
   Web of Science ®Google Scholar
• Kang, Y., and Zhou, L., Longer is better? A case study of product review helpfulness prediction. In Twenty-Second Americas Conference on Information Systems. San Diego, CA: AIS, 2016, pp. 1–10.
   Google Scholar
• Kang, Y., and Zhou, L. Helpfulness assessment of online reviews: the role of semantic hierarchy of product features. ACM Transactions on Management Information Systems, 10, 3 (2019), 12:11–12:18.
   PubMed Web of Science ®Google Scholar
• Karimi, S., and Wang, F. Online review helpfulness: impact of reviewer profile image. Decision Support Systems, 96(2017), 39–48.
   Web of Science ®Google Scholar
• Kelly, D. Methods for evaluating interactive information retrieval systems with users. Foundations and Trends^® in Information Retrieval, 3, 1–2 (2009), 1–224.
   Google Scholar
• Korfiatis, N., García-Bariocanal, E., and Sánchez-Alonso, S. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11, 3 (2012), 205–217.
   Web of Science ®Google Scholar
• Krishnamoorthy, S. Linguistic features for review helpfulness prediction. Expert Systems with Applications, 42, 7 (2015), 3751–3759.
   Web of Science ®Google Scholar
• Kuan, K.K.Y., Hui, K.-L., Prasarnphanich, P., and Lai, H.-Y. What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16, 1 (2015).
   Web of Science ®Google Scholar
• Kwon, B.C., Kim, S.-H., Duket, T., Catalán, A., and Yi, J.S. Do people really experience information overload while reading online reviews? International Journal of Human–Computer Interaction, 31, 12 (2015), 959–973.
   Web of Science ®Google Scholar
• Lazarsfeld, P.F., and Henry, N.W. Latent Structure Analysis. Boston: Houghton Mifflin, 1968.
   Google Scholar
• Lee, B.-K., and Lee, W.-N. The effect of information overload on consumer choice quality in an on-line environment. Psychology & Marketing, 21, 3 (2004), 159–183.
   Web of Science ®Google Scholar
• Lee, J., and Hong, I.B. Consumer’s electronic word-of-mouth adoption: The trust transfer perspective. International Journal of Electronic Commerce, 23, 4 (2019), 595–627.
   Web of Science ®Google Scholar
• Lee, S., and Choeh, J.Y. Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41, 6 (2014), 3041–3046.
   Web of Science ®Google Scholar
• Li, M., Huang, L., Tan, C.-H., and Wei, K.-K. Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17, 4 (2013), 101–136.
   Web of Science ®Google Scholar
• Li, S.-T., Pham, T.-T., and Chuang, H.-C. Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles. Information & Management, 56, 1 (2019), 28–38.
   Web of Science ®Google Scholar
• Liang, T.-P., Lai, H.-J., and Ku, Y.-C. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems, 23, 3 (2006), 45–70.
   Web of Science ®Google Scholar
• Liu, J., Cao, Y., Lin, C., Huang, Y., and Zhou, M., Low-quality product review detection in opinion summarization. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 2007, pp. 334–342.
   Google Scholar
• Loepp, B., Herrmanny, K., and Ziegler, J., Blended recommending: Integrating interactive information filtering and algorithmic recommender techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea: Association for Computing Machinery, 2015, pp. 975–984.
   Google Scholar
• Magidson, J., and Vermunt, J. Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20(2002), 37–44.
   Google Scholar
• Malik, M.S.I., and Hussain, A. An analysis of review content and reviewer variables that contribute to review helpfulness. Information Processing & Management, 54, 1 (2018), 88–104.
   Web of Science ®Google Scholar
• Moghaddam, S., Jamali, M., and Ester, M. ETF: Extended tensor factorization model for personalizing prediction of review helpfulness. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. Seattle: Association for Computing Machinery, 2012, pp. 163–172.
   Google Scholar
• Moore, S.G. Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions. Journal of Consumer Research, 42, 1 (2015), 30–44.
   Web of Science ®Google Scholar
• Mudambi, S.M., and Schuff, D. What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34, 1 (2010), 185–200.
   Web of Science ®Google Scholar
• Nunamaker, J.F., Briggs, R.O., Derrick, D.C., and Schwabe, G. The last research mile: Achieving both rigor and relevance in information systems research. Journal of Management Information Systems, 32, 3 (2015), 10–47.
   Web of Science ®Google Scholar
• Pan, Y., and Zhang, J.Q. Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87, 4 (2011), 598–612.
   Web of Science ®Google Scholar
• Park, D.-H., and Lee, J. eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7, 4 (2008), 386–398.
   Web of Science ®Google Scholar
• Park, S., and Nicolau, J.L. Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, Supplement C (2015), 67–83.
   Google Scholar
• Racherla, P., and Friske, W. Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11, 6 (2012), 548–559.
   Web of Science ®Google Scholar
• Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill, 1980.
   Google Scholar
• Salehan, M., and Kim, D.J. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81(2016), 30–40.
   Web of Science ®Google Scholar
• Sibte, S., and Abidi, S. Intelligent information personalization: from issues to strategies. In C. Mourlas and P. Germanakos (eds.), Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies. Hershey, PA: IGI Global, 2008, pp. 118–146.
   Google Scholar
• Singh, J.P., Irani, S., Rana, N.P., Dwivedi, Y.K., Saumya, S., and Kumar Roy, P. Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70(2017), 346–355.
   Web of Science ®Google Scholar
• Soto-Acosta, P., Jose Molina-Castillo, F., Lopez-Nicolas, C., and Colomo-Palacios, R. The effect of information overload and disorganisation on intention to purchase online: the role of perceived risk and internet experience. Online Information Review, 38, 4 (2014), 543–561.
   Web of Science ®Google Scholar
• Stai, E., Kafetzoglou, S., Tsiropoulou, E.E., and Papavassiliou, S. A holistic approach for personalization, relevance feedback & recommendation in enriched multimedia content. Multimedia Tools and Applications, 77, 1 (2018), 283–326.
   Web of Science ®Google Scholar
• Tam, K.Y., and Ho, S.Y. Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes. MIS Quarterly, 30, 4 (2006), 865–890.
   Web of Science ®Google Scholar
• Tang, J., Gao, H., Hu, X., and Liu, H. Context-aware review helpfulness rating prediction. In Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong, China: Association for Computing Machinery, 2013, pp. 1–8.
   Google Scholar
• Vermunt, J.K., and Magidson, J. Factor analysis with categorical indicators: A comparison between traditional and latent class approaches. In L. A. van der Ark, M. A. Croon, & K. Sijtsma (Eds.), New Developments in categorical Data Analysis for the Social and Behavioral Sciences. Mahwah, NJ: Erlbaum, 2005, 41–62.
   Google Scholar
• Wang, D., Zhu, S., and Li, T. SumView: A Web-based engine for summarizing product reviews and customer opinions. Expert Systems with Applications, 40, 1 (2013), 27–33.
   Web of Science ®Google Scholar
• Wang, X., Tang, L., and Kim, E. More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness? International Journal of Hospitality Management, 77(2019), 438–447.
   Web of Science ®Google Scholar
• Wattal, S., Telang, R., and Mukhopadhyay, T. Information personalization in a two-dimensional product differentiation model. Journal of Management Information Systems, 26, 2 (2009), 69–95.
   Web of Science ®Google Scholar
• Willemsen, L.M., Neijens, P.C., Bronner, F., and de Ridder, J.A. “Highly recommended!” The content characteristics and perceived usefulness of online consumer reviews. Journal of Computer-Mediated Communication, 17, 1 (2011), 19–38.
   Web of Science ®Google Scholar
• Xia, L., and Bechwati, N.N. Word of mouse: The role of cognitive personalization in online consumer reviews. Journal of Interactive Advertising, 9, 1 (2008), 3–13.
   Google Scholar
• Xie, K.L., Chen, C., and Wu, S. Online consumer review factors affecting offline hotel popularity: Evidence from Tripadvisor. Journal of Travel & Tourism Marketing, 33, 2 (2016), 211–223.
   Web of Science ®Google Scholar
• Xiong, W., and Litman, D., Automatically predicting peer-review helpfulness. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers. Portland, OR: Association for Computational Linguistics, 2011, pp. 502–507.
   Google Scholar
• Yin, D., Bond, S.D., and Zhang, H. Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38, 2 (2014), 539–560.
   Web of Science ®Google Scholar
• Yu, J., Zha, Z.-J., Wang, M., and Chua, T.-S., Aspect ranking: Identifying important product aspects from online consumer reviews. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, OR: Association for Computational Linguistics, 2011, pp. 1496–1505.
   Google Scholar