Similar Users-Augmented Interest Network

TL;DR

This paper introduces SUIN, a novel method that augments user behavior sequences with behaviors from similar users to improve CTR prediction accuracy in recommender systems.

Contribution

The paper proposes a new approach combining similar user behaviors with target user sequences, including position encoding and attention mechanisms, to enhance CTR prediction.

Findings

SUIN significantly outperforms state-of-the-art models on benchmark datasets.

Augmenting user sequences with similar users' behaviors improves prediction accuracy.

User-specific position encoding and attention effectively mitigate noise in augmented sequences.

Abstract

Click-through rate (CTR) prediction is one of the core tasks in recommender systems. User behavior sequences, as one of the most effective features, can accurately reflect user preferences and significantly improve prediction accuracy. Richer behavior sequences often enable more comprehensive user profiling, and recent studies have shown that scaling the length of user behavior sequence can yield substantial gains in CTR. However, due to the widespread sparsity in recommender systems, incomplete behavior sequences are common in real-world scenarios. Existing sequential modeling methods often rely solely on the target user's own behavior, and therefore struggle in such scenarios. This paper proposes a novel method called SUIN (Similar Users-augmented Interest Network), which enhances the target user's behavior sequence with behaviors from similar users to enhance the user profile for CTR prediction. Specifically, we use behavior embeddings encoded by a sequence encoder to retrieve users with similar behaviors from a user retrieval pool. The behavior sequences of these similar users are then concatenated with that of the target user in descending order of similarity to construct an augmented sequence. Given that the augmented sequence contains behaviors from multiple users, we propose a user-specific target-aware position encoding, which identifies the source user of each behavior and captures its relative position to the target item. Furthermore, to mitigate the empirically observed noise in similar users' behaviors, we design a user-aware target attention that jointly considers item-item and user-user correlations, fully exploiting the potential of the augmented behavior sequence. Comprehensive experiments on widely-used short-term and long-term sequence benchmark datasets demonstrate that our method significantly outperforms state-of-the-art sequential CTR models.