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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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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