New Recommendation Agent to Identify Innovators Utilizing User-Generated Content
Keywords:
business, economics, recommendation system, preference, user-generated content, social tag, e-commerceAbstract
Consumers who shop online are overwhelmed by the unlimited choices and options presented to them. Companies use different recommendation systems (agents), such as collaborative filtering that processes large volumes of data, to provide personalized and relevant recommendations for their customers to alleviate this problem. This paper explores a novel recommendation system that identifies consumers’ unique unobserved preferences by utilizing user-generated content (UGC), such as social tags or bookmarks. We propose better metrics and algorithms that can be used as recommendation agents for a successful new product campaign; our proposed method outperforms current popular recommendation algorithms (RA).
References
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Agrawal, S., Roy, D., & Mitra, M. (2021). Tag embedding based personalized point of interest recommendation system. Information Processing & Management, 58(6), 102690.
Ahn, H.J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37–51.
Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society. Series B (Methodological), 44(2), 139–177.
Bapna, R., & Umyarov, A. (2015). Do your online friends make you pay? A randomized field experiment on peer influence in online social networks. Management Science, 61(8), 1902–1920.
Carlson, E., Saari, P., Burger, B., & Toiviainen, P. (2017). Personality and musical preference using social-tagging in excerpt-selection. Psychomusicology: Music, Mind, and Brain, 27(3), 203.
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023). Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems, 41(3), 1–39.
Chen, Z., & Wang, S. (2022). A review on matrix completion for recommender systems. Knowledge and Information Systems, 64(1), 1–34.
Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343–362.
Datta, H., Knox, G., & Bronnenberg, B.J. (2017). Changing their tune: How consumers’ adoption of online streaming affects music consumption and discovery. Marketing Science, 37(1), 5–21.
Dong, J., Li, G., Ma, W., & Liu, J. (2022). Personalized recommendation system based on social tags in the era of Internet of Things. Journal of Intelligent Systems, 31(1), 681–689.
Ekstrand, M.D., Riedl, J.T., & Konstan, J.A. (2011). Collaborative filtering recommender systems. Foundations and Trends® in Human–Computer Interaction, 4(2), 81–173.
Garg, R., Smith, M.D., & Telang, R. (2011). Measuring information diffusion in an online community. Journal of Management Information Systems, 28(2), 11–38.
Ghazarian, S., & Nematbakhsh, M.A. (2015). Enhancing memory-based collaborative filtering for group recommender systems. Expert Systems with Applications, 42(7), 3801–3812.
Gong, S. (2009). Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation. JSW, 4(8), 883–890.
Greenberg, D.M., Baron-Cohen, S., Stillwell, D.J., Kosinski, M., & Rentfrow, P.J. (2015). Musical preferences are linked to cognitive styles. PloS One, 10(7), e0131151.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., & Riedl, J.T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5–53.
Hosanagar, K., Fleder, D., Lee, D., & Buja, A. (2014). Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Science, 60(4), 805–823.
Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems—beyond matrix completion. Communications of the ACM, 59(11), 94–102.
Koohi, H., & Kiani, K. (2020). Two new collaborative filtering approaches to solve the sparsity problem. Cluster Computing, 1–13.
Liebman, E., Saar-Tsechansky, M., & Stone, P. (2014). Dj-mc: A reinforcement-learning agent for music playlist recommendation. arXiv preprint arXiv:1401.1880.
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.
Mahajan, V., & Muller, E. (1998). When is it worthwhile targeting the majority instead of the innovators in a new product launch? Journal of Marketing Research, 35(4), 488–495.
Martín-Fernández, J., Barceló-Vidal, C., Pawlowsky-Glahn, V., Buccianti, A., Nardi, G., & Potenza, R. (1998). Measures of difference for compositional data and hierarchical clustering methods. Paper presented at the Proceedings of IAMG.
Najafabadi, M.K., Mohamed, A., & Onn, C.W. (2019). An impact of time and item influencer in collaborative filtering recommendations using graph-based model. Information Processing & Management, 56(3), 526–540.
Nam, H., Joshi, Y.V., & Kannan, P. (2017). Harvesting brand information from social tags. Journal of Marketing, 81(4), 88–108.
Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A.H. (2020). Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data. Expert Systems with Applications, 149, 113248.
Otero, N., Tolosana-Delgado, R., Soler, A., Pawlowsky-Glahn, V., & Canals, A. (2005). Relative vs. absolute statistical analysis of compositions: A comparative study of surface waters of a Mediterranean river. Water Research, 39(7), 1404–1414.
Pathak, B., Garfinkel, R., Gopal, R.D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159–188.
Pyo, T.-H., Lee, J.Y., & Park, H.M. (2022). The Effects of Consumer Preference and Peer Influence on Trial of an Experience Good. Journal of Marketing Research, 59(6), 1161–1178.
Rentfrow, P.J., & Gosling, S.D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236.
Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. Recommender Systems Handbook (pp. 1–35). Springer.
Ringel, D.M., & Skiera, B. (2016). Visualizing asymmetric competition among more than 1,000 products using big search data. Marketing Science, 35(3), 511–534.
Robertson, T.S. (1967). Consumer innovators: the key to new product success. California Management Review, 10(2), 23–30.
Sachan, A., & Richhariya, V. (2013). Reduction of data sparsity in collaborative filtering based on fuzzy inference rules. International Journal of Advanced Computer Research, 3(2), 101.
Thorat, P.B., Goudar, R.M., & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 31–36.
Vaughan, P. (2017). Half of Your Customers Spend 75% of Time Conducting Research Online.
Wilson, J. (2022). The age of digital; music executive reacts to the impact of digitalization in the music industry.
Wu, S., Sun, F., Zhang, W., Xie, X., & Cui, B. (2022). Graph neural networks in recommender systems: A survey. ACM Computing Surveys, 55(5), 1–37.
Yildirim, H., & Krishnamoorthy, M.S. (2008). A random walk method for alleviating the sparsity problem in collaborative filtering. Paper presented at the Proceedings of the 2008 ACM Conference on Recommender Systems.
Zheng, N., & Li, Q. (2011). A recommender system based on tag and time information for social tagging systems. Expert Systems with Applications, 38(4), 4575–4587.
Zhou, C., Leng, M., Liu, Z., Cui, X., & Yu, J. (2022). The impact of recommender systems and pricing strategies on brand competition and consumer search. Electronic Commerce Research and Applications, 53, 101144.
