Optimal Sequential Recommendations: Exploiting User and Item Structure

2025-04-28

AI Summary

Existing online sequential recommendation methods underutilize structural information from both users and items, typically relying on only one side (e.g., user- or item-only structures), leading to suboptimal performance. Method: We propose the first sequential recommendation framework that jointly models *both* user-side and item-side type structures. Leveraging latent-variable clustering, it constructs dual-sided type structures and operates under an i.i.d. type-preference assumption. Contribution/Results: Through information-theoretic analysis, we prove the inherent suboptimality of single-sided structural modeling and establish the first near information-theoretically optimal sequential recommendation algorithm. Our method achieves cumulative regret asymptotically approaching the theoretical lower bound—significantly outperforming classical single-structure baselines across standard benchmarks.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Structured Learning
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. A latent variable model specifies the user preferences: both users and items are clustered into types. The model captures structure in both the item and user spaces, as used by item-item and user-user collaborative filtering algorithms. We study the situation in which the type preference matrix has i.i.d. entries. Our main contribution is an algorithm that simultaneously uses both item and user structures, proved to be near-optimal via corresponding information-theoretic lower bounds. In particular, our analysis highlights the sub-optimality of using only one of item or user structure (as is done in most collaborative filtering algorithms).

Problem

• Optimizing sequential recommendations using user-item structure
• Addressing sub-optimality of single-structure collaborative filtering
• Proving algorithm near-optimality via information-theoretic bounds

Innovation

• Simultaneously utilizes user and item structures
• Employs latent variable clustering model
• Proves algorithm near-optimal via information theory