Use of clustering for consideration set modeling in recommender systems
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2021-01-05
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4270
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Abstract
The cold-start problem has become a significant challenge in recommender systems. To solve this problem, most approaches use various user-side data and combine them with item-side information in their systems design. However, when such user data is not available, those methods become unfeasible. We provide a novel recommender system design approach which is based on two-stage decision heuristics. By utilizing only the item-side characteristics we first identify the structure of the final choice set and then generate it using stochastic and deterministic approaches.
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Electronic Marketing, clustering, cold-start problem, recommender systems, two-stage choice
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9 pages
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Proceedings of the 54th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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