RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation

TL;DR

RUEL is a retrieval-augmented sequential recommender that leverages anonymous Edge browser logs to improve user preference modeling, addressing data sparsity without requiring cross-platform user IDs.

Contribution

It introduces a novel retrieval-based framework that incorporates external anonymous browsing data into recommendation systems, enhancing performance across various datasets.

Findings

RUEL significantly outperforms state-of-the-art baselines.

The retrieval mechanism effectively enriches user representations.

Ablation studies validate the importance of each component.

Abstract

Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately under the condition of data sparsity. To address this challenge, some methods have leveraged external user behavior data from multiple platforms to enrich user representation. However, all of these methods require a consistent user ID across platforms and ignore the information from similar users. In this study, we propose RUEL, a novel retrieval-based sequential recommender that can effectively incorporate external anonymous user behavior data from Edge browser logs to enhance recommendation. We first collect and preprocess a large volume of Edge browser logs over a one-year period and link them to target entities that correspond to candidate items in recommendation datasets. We then design a contrastive learning framework with a momentum encoder and a memory bank to retrieve the most relevant and diverse browsing sequences from the full browsing log based on the semantic similarity between user representations. After retrieval, we apply an item-level attentive selector to filter out noisy items and generate refined sequence embeddings for the final predictor. RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets. We conduct extensive experiments on four real datasets for sequential recommendation tasks and demonstrate that RUEL significantly outperforms state-of-the-art baselines. We also conduct ablation studies and qualitative analysis to validate the effectiveness of each component of RUEL and provide additional insights into our method.