Time-Interval-Aware Disentangled Expert Modeling for Next-Basket Recommendation

• URL: http://arxiv.org/abs/2605.00499v1
• Date: Fri, 01 May 2026 08:20:49 GMT
• Title: Time-Interval-Aware Disentangled Expert Modeling for Next-Basket Recommendation
• Abstract summary: Next-basket (NBR) is a type of recommendation that aims to predict a set of items a user will purchase based on their historical transaction basket sequences.<n>It is governed by a dynamic interplay between two distinct user intents: habitual repurchase, and exploratory interest.<n>We propose TimeInterval Disentangled Experts (TIDE) to address these challenges.
• Score: 16.792016367192392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Next-basket recommendation (NBR) is a type of recommendation that aims to predict a set of items a user will purchase based on their historical transaction basket sequences. It is governed by a dynamic interplay between two distinct user intents: habitual repurchase, which involves repeating past behaviors, and exploratory interest, which involves discovering new items. However, existing NBR methods generally suffer from two limitations: (1) they often entangle these conflicting motives within a single representation, causing habits to overshadow discovery, and (2) they rely on discrete sequential modeling that ignores continuous-time intervals and item-specific periodicities. In this paper, we propose a novel solution named Time-Interval Disentangled Experts (TIDE) to address these challenges. TIDE incorporates a Hawkes-enhanced Fourier Time Encoding to capture item-specific temporal periodicities and dynamic decay. To decouple user intentions, TIDE utilizes a dual-expert architecture that integrates a Habit Expert for recurring needs and a Pattern-Guided Exploration Expert for discovery. Combined with an item-aware gating mechanism, TIDE adaptively balances repurchase and exploration. Extensive experiments on four diverse real-world datasets demonstrate that TIDE consistently outperforms representative state-of-the-art NBR methods.

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