Abstract
A recommender system supports customers to find information, products, or services (such as music, books, movies, web sites, and digital contents), so it could help customers to make rapid routine decisions and save their time and money. However, most existing recommender systems do not recommend items that are already purchased by the target customer, so are not suitable for considering customers’ repetitive purchase behavior or purchasing order. In this research, we suggest a multi-period product recommender system, which can learn customers’ purchasing order and customers’ repetitive purchase pattern. For such a purpose we applied the Recurrent Neural Network (RNN), which is one of the artificial neural network structures specialized in time series data analysis, instead of collaborative filtering techniques. Recommendation periods are segmented as various time-steps, and the proposed RNN-based recommender system can recommend items by multiple periods in a time sequence. Several experiments with real online food market data show that the proposed system shows higher performance in accuracy and diversity in a multi-period perspective than the collaborative filtering-based system. From the experimental results, we conclude that the proposed system is suitable for multi-period product recommendation, which results in robust performance considering well customers’ purchasing orders and customers’ repetitive purchase patterns. Moreover, in terms of sustainability, we expect that our study contributes to the reduction of food wastes by inducing planned consumption, and the reduction of shopping time and effort.
Highlights
As the interest in personalization services increases in various fields, recommender systems applying various knowledge discovery techniques are being studied commercially and academically [1]
Collaborative filtering (CF) is a technique known as showing the best performance in product recommender systems [3,4]
There is a need for recommender systems that can process and analyze a huge amount of data and recommend the multi-period products needed in everyday life
Summary
As the interest in personalization services increases in various fields, recommender systems applying various knowledge discovery techniques are being studied commercially and academically [1]. Product recommendation systems, mostly developed based on online commerce, have been gradually becoming important in terms of sales and customer relationship as well as helping consumers to choose [2]. Collaborative filtering (CF) is a technique known as showing the best performance in product recommender systems [3,4]. CF-based recommendation models predict preference based on the similarity between users or items, but scalability and sparsity problems may have occurred due to data increases as e-commerce grows [4]. There is a need for recommender systems that can process and analyze a huge amount of data and recommend the multi-period products needed in everyday life. Multi-period product recommender systems reflect purchasing patterns of customers, but traditional CF-based recommender systems do not consider well customers’ purchasing orders [7]
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