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http://dx.doi.org/10.13088/jiis.2022.28.1.311

Analysis of the Effects of E-commerce User Ratings and Review Helfulness on Performance Improvement of Product Recommender System  

FAN, LIU (Department of Business Administration, Graduate School, Kyung Hee University)
Lee, Byunghyun (Department of Big Data Analytics, Graduate School, Kyung Hee University)
Choi, Ilyoung (Graduate School of Business Administration, Kyung Hee University)
Jeong, Jaeho (Department of Business Administration, Graduate School, Kyung Hee University)
Kim, Jaekyeong (School of Management & Department of Big Data Analytics, Graduate School, Kyung Hee University)
Publication Information
Journal of Intelligence and Information Systems / v.28, no.1, 2022 , pp. 311-328 More about this Journal
Abstract
Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.
Keywords
E-Commerce; Recommender System; Amazon Data; Review Helpfulness; Collaborative Filtering;
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1 Qiu, J., Q. Wu, G. Ding, Y. Xu, and S. Feng, "A survey of machine learning for big data processing," EURASIP Journal on Advances in Signal Processing, Vol.2016, No.1(2016), 1-16.   DOI
2 Abdollahi, B., and O. Nasraoui, "Using explainability for constrained matrix factorization," In Proceedings of the Eleventh ACM Conference on Recommender Systems, (2017), 79-83.
3 Al-Bashiri, H., M. A. Abdulgabber, A. Romli, and H. Kahtan, "An improved memory-based collaborative filtering method based on the TOPSIS technique," PloS one, Vol.13, No.10 (2018), e0204434.   DOI
4 Bang, H., H. Lee, and J. H. Lee, "TV Program recommender system using viewing time patterns," Journal of the Korean Institute of Intelligent Systems, Vol.25, No.5(2015), 431-436.   DOI
5 Castelli, M., L. Manzoni, L. Vanneschi, and A. Popovic, "An expert system for extracting knowledge from customers' reviews: The case of amazon. com, inc.," Expert Systems with Applications, Vol.84, 117-126.   DOI
6 Choeh, J. Y., S. K. Lee, and Y. B. Cho, "Applying rating score's reliability of customers to enhance prediction accuracy in recommender system," The Journal of the Korea Contents Association, Vol.13, No.7(2013), 379-385.   DOI
7 Eslami, S. P., M. Ghasemaghaei, and K. Hassanein, "Which online reviews do consumers find most helpful? A multi-method investigation," Decision Support Systems, Vol.113, (2018) 32-42.   DOI
8 Ge, S., T. Qi, C. Wu, F. Wu, X. Xie, and Y. Huang, "Helpfulness-aware review based neural recommendation," CCF transactions on pervasive computing and interaction, Vol.1, No.4(2019), 285-295.   DOI
9 Koohi, H., & K. Kiani, "User based collaborative filtering using fuzzy C-means," Measurement, 91, (2016), 134-139.   DOI
10 Pradel, B., N. Usunier, and P. Gallinari, "Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics," In Proceedings of the sixth ACM conference on Recommender systems, (2012), 147-154
11 Qiu, J., J. Wang, S. Yao, K. Guo, B. Li, E. Zhou, and H. Yang, "Going deeper with embedded fpga platform for convolutional neural network," In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (2016), 26-35.
12 Ren, G., and T. Hong, "Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews," Information Processing & Management, Vol.56, No.4(2019), 1425-1438.   DOI
13 Shani, G., and A. Gunawardana, "Evaluating recommendation systems," In Recommender systems handbook, Springer, Boston, 2011.
14 Smith, B., and G. Linden, "Two decades of recommender systems at Amazon. com," Ieee internet computing, Vol.21, No.3(2017), 12-18.   DOI
15 Sun, Y., Z. Wang, P. Fu, Q. Jiang, T. Yang, J. Li, and X. Ge, "The impact of relative humidity on aerosol composition and evolution processes during wintertime in Beijing, China," Atmospheric Environment, Vol.77, (2013), 927-934.   DOI
16 Thakkar, P., K. Varma, V. Ukani, S. Mankad, and S. Tanwar, "Combining user-based and item-based collaborative filtering using machine learning," In Information and Communication Technology for Intelligent Systems, Springer, Singapore, 2019.
17 Al-Smadi, M., B. Talafha, M. Al-Ayyoub, and Y. Jararweh, "Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews," International Journal of Machine Learning and Cybernetics, Vol.10, No.8(2019), 2163-2175.   DOI
18 Gomez-Uribe, C. A., and N. Hunt, "The netflix recommender system: Algorithms, business value, and innovation," ACM Transactions on Management Information Systems (TMIS), Vol.6, No.4(2015), 1-19.
19 Chintagunta, P. K., S. Gopinath, and S. Venkataraman, "The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets," Marketing science, Vol.29, No.5(2010), 944-957.   DOI
20 Fang, Q., C. Xu, M. S. Hossain, and G. Muhammad, "Stcaplrs: A spatial-temporal context-aware personalized location recommendation system," ACM Transactions on Intelligent systems and technology (TIST), Vol.7, No.4(2016), 1-30.
21 Linden, G., B. Smith, and J. York, "Amazon. com recommendations: Item-to-item collaborative filtering," IEEE Internet computing, Vol.7, No.1(2003), 76-80.   DOI
22 Nakayama, M., and Y. Wan, "The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews," Information & Management, Vol.56, No.2(2019), 271-279.   DOI
23 Goldberg, L. R., "The development of markers for the Big-Five factor structure," Psychological assessment, Vol.4, No.1(1992), 26.   DOI
24 Shengli, L., and L. Fan, "The interaction effects of online reviews and free samples on consumers' downloads: An empirical analysis," Information Processing & Management, Vol.56, No.6(2019), 102071.   DOI
25 Su, X., and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in artificial intelligence, (2009), 2009.
26 Wang, Y., J. Wang, and T. Yao, "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Vol.19, No.2(2019), 257-284.   DOI
27 Moore, S. G., "Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions," Journal of Consumer Research, Vol.42, No.1(2015), 30-44.   DOI
28 Lu, L., W. Xu, and S. Qiao, "A fast SVD for multilevel block Hankel matrices with minimal memory storage," Numerical Algorithms, Vol.69, No.4(2015), 875-891.   DOI
29 Yun, Y., D. Hooshyar, J. Jo, and H. Lim, "Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review," Journal of Information Science, Vol.44 No.3(2018), 331-344.   DOI
30 Srifi, M., A. Oussous, A. Ait Lahcen, and S. Mouline, "Recommender systems based on collaborative filtering using review texts?A survey," Information, Vol.11, No.6(2020), 317.   DOI
31 Lee, S. H., A. R. Jo, and H. Y. Lee, "The Medical Service Customers Satisfaction Factors Extracted from Online Hospital Review Data Using Latent DirichletAllocation Method," Journal of Korea Service Management Society, Vol.18, No.5(2017), 23-44.   DOI
32 Zarzour, H., Z. Al-Sharif, M. Al-Ayyoub, and Y. Jararweh, "A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques," In 2018 9th international conference on information and communication systems (ICICS), (2018), 102-106.