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DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation

Jiawei Cheng, Min Gao, Zongwei Wang, Xiaofei Zhu, Zhiyi Liu, Wentao Li, Wei Li, Huan Wu

TL;DR

DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account.

Abstract

Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often assume a fixed number of latent users per account, limiting their ability to adapt to diverse sharing patterns and reducing recommendation accuracy. Recent latent reasoning technique applied in sequential recommendation (SR) generate intermediate embeddings from the user embedding (e.g, last item embedding) to uncover users' potential interests, which inspires us to treat the problem of inferring the number of latent users as generating a series of intermediate embeddings, shifting from inferring preferences behind user to inferring the users behind account. However, the last item cannot be directly used for reasoning in SSR, as it can only represent the behavior of the most recent latent user, rather than the collective behavior of the entire account. To address this, we propose DisenReason, a two-stage reasoning method tailored to SSR. DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account. Experiments on four benchmark datasets show that DisenReason consistently outperforms all state-of-the-art baselines across four benchmark datasets, achieving relative improvements of up to 12.56\% in MRR@5 and 6.06\% in Recall@20.

DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation

TL;DR

DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account.

Abstract

Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often assume a fixed number of latent users per account, limiting their ability to adapt to diverse sharing patterns and reducing recommendation accuracy. Recent latent reasoning technique applied in sequential recommendation (SR) generate intermediate embeddings from the user embedding (e.g, last item embedding) to uncover users' potential interests, which inspires us to treat the problem of inferring the number of latent users as generating a series of intermediate embeddings, shifting from inferring preferences behind user to inferring the users behind account. However, the last item cannot be directly used for reasoning in SSR, as it can only represent the behavior of the most recent latent user, rather than the collective behavior of the entire account. To address this, we propose DisenReason, a two-stage reasoning method tailored to SSR. DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account. Experiments on four benchmark datasets show that DisenReason consistently outperforms all state-of-the-art baselines across four benchmark datasets, achieving relative improvements of up to 12.56\% in MRR@5 and 6.06\% in Recall@20.
Paper Structure (26 sections, 17 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 26 sections, 17 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sequential Recommendation
  4. Reasoning-based Recommendation
  5. Fourier Transform in Recommendation
  6. Research Objective
  7. Preliminaries
  8. Problem Formulation
  9. Fourier Transform
  10. Proposed Framework: DisenReason
  11. Stage One: Behavior Disentanglement Reasoning for Pivot
  12. Stage Two: Progressive Residual Reasoning for Latent User
  13. Training Objective
  14. Experiments
  15. Experimental Settings
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Visualization of frequency-domain disentanglement for shared-account behavior sequence.
  • Figure 2: Overview of the proposed DisenReason framework. This figure shows the details of DisenReason: stage one focuses on disentangling the mixed behavioral sequence to create a unified account pivot, while stage two progressively infers latent users based on this pivot.
  • Figure 3: Hyperparameter analysis of DisenReason on HV-V and HV-E dataset.
  • Figure 4: Performance comparison of DisenReason and baselines under different sequence lengths and training data scales on HV-V and HV-E datasets.
  • Figure 5: Latent user number per account ID on HV-E and HV-V dataset. It illustrates the number of latent users inferred for each account ID through the reasoning process.