ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping

Ariannezhad, Mozhdeh; Jullien, Sami; Li, Ming; Fang, Min; Schelter, Sebastian; Rijke, Maarten de

doi:10.1145/3477495.3531708

Cited by 35 publications

(12 citation statements)

References 18 publications

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“…Faggioli et al [7] introduce UP-CF@r, which considers item popularity, recency of items, and similar users' preferences. ReCANet [3] proposed by Ariannezhad et al considers the periodicity of users' repeated purchase behavior, achieving outstanding performance. However, these methods mainly focus on items users have previously purchased and fail to capture the temporal dependencies within users' historical basket sequences, thereby limiting the model performances.…”

Section: Next-basket Recommendationmentioning

confidence: 99%

“…Next-basket recommendation (NBR) has been widely applied in the e-commerce and retail sectors, drawing an increasing number of researchers to study this field [1][2][3][4]. In NBR, each purchase record by a user consists of multiple items, forming a basket where the items do not possess a sequential order.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies [2,3] indicate that repeated purchases widely exist in users' historical purchase records. Leveraging this repeated purchase information can yield substantial performance improvement in NBR.…”

Section: Introductionmentioning

confidence: 99%

“…When people shop at retail stores, they repeatedly purchase items they prefer. Focusing on items that users repeatedly purchase can effectively enhance recommendation performance [3,4]. Some research [4,7] have utilized users' repeated purchase information, combining it with collaborative filtering to generate recommendations, achieving promising results.…”

Section: Introductionmentioning

confidence: 99%

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Contrastive Learning for Next-basket Recommendation

Zhu,

Wang,

Zhang

et al. 2024

Preprint

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Next-basket recommendation (NBR) aims to recommend items to a user based on his/her historical basket sequence. Existing methods typically employ neural networks to model a user's historical basket sequence. While achieving promising results, these methods overlook the data sparsity problem inherent in NBR. Data sparsity problem prevents models from obtaining sufficient training. To alleviate the data sparsity problem in NBR, we propose a multi-task learning framework named Contrastive Learning for Next-basket Recommendation (CL4NBR). CL4NBR introduces a contrastive learning task alongside the original next-basket prediction task, leveraging self-supervised signals from users' historical basket sequences. This approach enables the generation of more effective user representations, leading to more accurate recommendation results. To obtain self-supervised signals from users' basket sequences, three data augmentation methods for basket sequences are designed in this study. Furthermore, CL4NBR utilizes the repeated purchase information of users when predicting their next baskets. Experimental evaluations conducted on four public datasets demonstrate that CL4NBR outperforms state-of-the-art methods.

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Section: Next-basket Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contrastive Learning for Next-basket Recommendation

Zhu,

Wang,

Zhang

et al. 2024

Preprint

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“…Next basket recommendation (NBR) is a type of sequential recommendation that caters to this scenario: baskets are the target of recommendation and historical sequential data consists of users' interactions with baskets. NBR has increasingly been attracting attention in recent years [2]. Many methods, based on different machine learning techniques, have been proposed for accurate recommendations, e.g., Markov chain (MC)-based methods [37,41], frequency and nearest neighbor-based methods [12,15], RNN-based methods [14,19,34,49], and self-attention methods [9,39,50].…”

Section: Introductionmentioning

confidence: 99%

A Next Basket Recommendation Reality Check

Jullien

Ariannezhad

et al. 2023

ACM Trans. Inf. Syst.

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The goal of a next basket recommendation system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. We examine whether the performance gains of the next basket recommendation (NBR) methods reported in the literature hold up under a fair and comprehensive comparison. To clarify the mixed picture that emerges from our comparison, we provide a novel angle on the evaluation of NBR methods, centered on the distinction between repetition and exploration: the next basket is typically composed of previously consumed items (i.e., repeat items) and new items (i.e., explore items). We propose a set of metrics that measure the repetition/exploration ratio and performance of NBR models. Using these new metrics, we provide a second analysis of state-of-the-art NBR models. The results help to clarify the extent of the actual progress achieved by existing NBR methods as well as the underlying reasons for any improvements that we observe. Overall, our work sheds light on the evaluation problem of NBR, provides a new evaluation protocol, and yields useful insights for the design of models for this task.

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