Group recommender system for store product placement

• Autores: Jihoi Park, Kihwan Nam
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 33, Nº 1, 2019, págs. 204-229
• Idioma: inglés
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• Resumen

  • We propose a group recommender system considering the recommendation quantity and repeat purchasing by using the existing collaborative filtering algorithm in order to optimize the offline physical store inventories. This research is the first of its kind to consider recommendation quantity and repetitive recommendations when creating group recommender systems. In offline stores, physical limitations result in the ability to display only a limited number of items. Quantity and selection of the item is an important decision for offline stores. In this paper, we suggest applying the user-based recommender system, which is capable of determining the best suited recommendation items for each store. This model is evaluated by the MAE, precision, recall, and F1 measures, and shows higher performance than the baseline model. A new performance evaluation measure is also suggested in this research. New quantity precision, quantity recall, and quantity F1 measures consider a penalty for a shortage or excess of the recommendation quantity. Novelty is defined as the proportion of items that the consumer may not have experienced in the recommendation list. Through the use of this novelty measure, we assess the new profit creation effect of the suggested model. Finally, previous research focused on recommendations for online customers, however, we expanded the recommender system to incorporate offline stores. This research is not only an academic contribution to the marketing field, but also practical contribution to offline stores through the usability of a developed offline shopping algorithm.