Session-Based Recommendations
72 papers with code • 7 benchmarks • 3 datasets
Recommendation based on a sequence of events. e.g. next item prediction
Libraries
Use these libraries to find Session-Based Recommendations models and implementationsLatest papers
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history.
On the Effectiveness of Unlearning in Session-Based Recommendation
On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session.
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.
Context-aware Session-based Recommendation with Graph Neural Networks
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session.
Beyond Co-occurrence: Multi-modal Session-based Recommendation
(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?
SR-PredictAO: Session-based Recommendation with High-Capability Predictor Add-On
In this framework, we propose a high-capability predictor module which could alleviate the effect of random user's behavior for prediction.
Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy.
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.