ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

Abstract

A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.

AAAI 2018

References in corpus (7)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Convolutional Neural Networks for Sentence Classification
• Wide & Deep Learning for Recommender Systems
• Long Short-Term Memory-Networks for Machine Reading
• Joint Deep Modeling of Users and Items Using Reviews for Recommendation
• Deep Interest Network for Click-Through Rate Prediction

Cited by in corpus (18)

• Deep Learning based Recommender System: A Survey and New Perspectives
• Personalized Bundle List Recommendation
• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• Deep Interest Network for Click-Through Rate Prediction
• Controllable Multi-Interest Framework for Recommendation
• Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
• Pairwise Interactive Graph Attention Network for Context-Aware Recommendation
• Denoising User-aware Memory Network for Recommendation
• UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
• NAIRS: A Neural Attentive Interpretable Recommendation System
• Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution
• Attribute-aware Collaborative Filtering: Survey and Classification
• A Domain Generalization Perspective on Listwise Context Modeling
• Deep neural network marketplace recommenders in online experiments
• Consistency-Aware Recommendation for User-Generated ItemList Continuation
• Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
• Context-aware short-term interest first model for session-based recommendation
• ET-USB: Transformer-Based Sequential Behavior Modeling for Inbound Customer Service