Abstract
Group recommendation systems, which deliver items to a group of users, have recently received a lot of interest. Several aggregation and model group recommendation techniques were discussed. On the other hand, group recommendation's cold-start issue has received less attention, severely restricting group recommendation in several crucial areas, such as offline suggestions. In this study, we offer a new deep hybrid framework to address the cold start issue with group event recommendations for a user group. Our framework is the basis for RBM and comprises numerous restricted Boltzmann machines (RBM). The first gathers client preferences as well as high-quality latent data. Context information like location and event structure is used to identify late event aspects. Set up a schedule for the event. To fix the cold-start issue, we tested our proposed framework on two real-world datasets, and the results demonstrate that it outperforms Baseline Group recommendation techniques and efficiently addresses the cold-start issue in group events.
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Sharma, S., Shakya, H.K. (2023). Recommendation Systems for a Group of Users Which Recommend Recent Attention: Using Hybrid Recommendation Model. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_58
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