| dc.description.abstract | The next basket recommendation aims to recommend items for the user′s next basket based on their past basket history. However, previous basket recommendation methods often simply recommend the top N predicted items directly after learning from the user′s purchase history, without considering the complementary and substitution relationships between items. Complementary relationship refers to the frequent co-purchasing of two items, while substitution relationship indicates that two items can be interchangeably chosen. To consider the interplay between items, previous research has typically relied on association rule-based recommendation systems. However, these methods only consider item associations and do not consider the sequential relationship between user baskets and user preferences.
Therefore, this study proposes an Association-based Sequential Basket Recommendation model, ASBRec, which utilizes sequence-based models to construct a basket recommendation system for predicting the items that users are likely to purchase in the next basket. Notably, we incorporate considerations of basket associations in both the frontend and backend of the model. At the frontend, we encode items using the Item2Vec method, incorporating associations into item embeddings. This helps capture the similarity and complementarity between items. At the backend, we utilize association rules to establish a confidence matrix that captures the confidence between all items. We then aggregate the confidence matrix with the output sequence, adjusting the weights between confidence and the output sequence to refine the basket recommendation results and enhance their associations. This sequence-based basket recommendation approach combines item relationships and user preferences, providing more accurate and personalized recommendations. In this study, we conducted experiments on a real-world dataset, and the results demonstrate that our proposed method outperforms previous approaches in terms of recommendation performance. | en_US |