Abstract
With companies focusing intensively on customer experience, personalization and platform usability have become crucial for a company’s success. Hence, providing appropriate recommendations to users is a challenging problem in various industries. We work toward enhancing the recommendation system of a timeshare exchange platform by leveraging real-time search data. Previously, the recommendation model utilized historical data to recommend resorts to users and was deployed online once a day. The limitation of this model was that it did not consider the real-time searches of the user, hence losing context. This directly impacted the click-through rate of the recommendations, and the users had to navigate the website excessively to find a satisfactory resort. We build a model such that it utilizes not only the historical transactional and master data but also the real-time search data to provide multiple relevant resort recommendations within 5 s.
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References
Personalized Product Recommendations Drive Just 7% of Visits but 26% of Revenue (n.d.). Retrieved from https://www.salesforce.com/blog/2017/11/personalized-product-recommendations-drive-just-7-visits-26-revenue.html
How Netflix’s Recommendations System Works (n.d.). Retrieved from https://help.netflix.com/en/node/100639
E. Keller, Recommendations are what drives your business. Remember to ask for them. (2012, July 27). Retrieved from https://www.forbes.com/sites/kellerfaygroup/2012/07/25/recommendations-are-what-drives-your-business-remember-to-ask-for-them/#62fa5f9539c6
H. Deng, Recommender systems in practice. (2019, December 5). Retrieved from https://towardsdatascience.com/recommender-systems-in-practice-cef9033bb23a
Challenges & Solutions for Production Recommendation Systems – Data Revenue Blog (n.d.). Retrieved from https://www.datarevenue.com/en-blog/building-a-production-ready-recommendation-system
C. Shahabi, F. Banaei-Kashani, Y.-S. Chen, D. McLeod, Yoda: An accurate and scalable web-based recommendation system, in Cooperative Information Systems. CoopIS 2001, vol. 2172, (Springer, Berlin/Heidelberg, 2001), pp. 418–432. https://doi.org/10.1007/3-540-44751-2_31
X. Jin, Y. Zhou, B. Mobasher, A maximum entropy web recommendation system: Combining collaborative and content features, in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2005), pp. 612–617. https://doi.org/10.1145/1081870.1081945
Y. Hu, Y. Koren, C. Volinsky, Collaborative filtering for implicit feedback datasets, in 2008 Eighth IEEE International Conference on Data Mining, (Pisa, 2008), pp. 263–272
G. Huming, L. Weili, A hotel recommendation system based on collaborative filtering and Rankboost algorithm, in 2010 Second International Conference on Multimedia and Information Technology, (Kaifeng, 2010), pp. 317–320
B. Chandramouli, J. Levandoski, A. Eldawy, M. Mokbel, StreamRec: A real-time recommender system, in ACM SIGMOD International Conference on Management of Data (SIGMOD 2011), (2011), pp. 1243–1246. https://doi.org/10.1145/1989323.1989465
D.A. Adeniyi, Z. Wei, Y. Yongquan, Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl. Comput. Inform. 12(1), 90–108, ISSN 2210-8327 (2016). https://doi.org/10.1016/j.aci.2014.10.001. http://www.sciencedirect.com/science/article/pii/S221083271400026X
D. Gavalas, C. Konstantopoulos, K. Mastakas, G. Pantziou, Mobile recommender systems in tourism. J. Netw. Comput. Appl. 39, 319–333, ISSN 1084 8045 (2014). https://doi.org/10.1016/j.jnca.2013.04.006. http://www.sciencedirect.com/science/article/pii/S1084804513001094
T. Chen, Y.H. Chuang, Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation. J. Ambient. Intell. Humaniz. Comput. 9, 275–284 (2018). https://doi.org/10.1007/s12652-015-0335-2
Y. Huang, B. Cui, W. Zhang, J. Jiang, X. Ying, TencentRec: Real-time stream recommendation in practice, in SIGMOD ‘15, (2015), pp. 227–238. https://doi.org/10.1145/2723372.2742785
Y. Lin, T. Chen, L. Wang, Integer nonlinear programming and optimized weighted-average approach for mobile hotel recommendation by considering travelers’ unknown preferences. Oper. Res. Int. J. 18, 625–643 (2018). https://doi.org/10.1007/s12351-016-0270-9
Arruza, Michael, John Pericich and Michael Straka. “The Automated Travel Agent: Hotel Recommendations Using Machine Learning.” (2016)
Y.-C. Lin, T. Chen, L.-C. Wang, Integer nonlinear programming and optimized weighted-average approach for mobile hotel recommendation by considering travelers’ unknown preferences. Oper. Res. Springer 18(3), 625–643 (2018, October)
A. Mavalankar, A. Gupta, C. Gandotra, R. Misra, Hotel recommendation system. Comput. Sci. Math. (2019). https://doi.org/10.13140/RG.2.2.27394.22728/1
M. Ludewig, D. Jannach, Learning to rank hotels for search and recommendation from session-based interaction logs and meta data, in Proceedings of the Workshop on ACM Recommender Systems Challenge (RecSys Challenge ‘19), (Association for Computing Machinery, New York, Article 5, 2019), pp. 1–5. https://doi.org/10.1145/3359555.3359561
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Tyagi, H., Goyal, M.P., Jindal, R., Lanham, M.A., Shrestha, D. (2021). A Personalized Recommender System Using Real-Time Search Data Integrated with Historical Data. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_16
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