The world is binary: Contrastive learning for denoising next basket recommendation
Next basket recommendation aims to infer a set of items that a user will purchase at the next
visit by considering a sequence of baskets he/she has purchased previously. This task has …
visit by considering a sequence of baskets he/she has purchased previously. This task has …
Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation
Session-based recommendation plays a central role in a wide spectrum of online
applications, ranging from e-commerce to online advertising services. However, the majority …
applications, ranging from e-commerce to online advertising services. However, the majority …
Attention-based dynamic user modeling and deep collaborative filtering recommendation
R Wang, Z Wu, J Lou, Y Jiang - Expert Systems with Applications, 2022 - Elsevier
Deep learning (DL) techniques have been widely used in recommender systems for user
modeling and matching function learning based on historical interaction matrix. However …
modeling and matching function learning based on historical interaction matrix. However …
Dual sparse attention network for session-based recommendation
J Yuan, Z Song, M Sun, X Wang… - Proceedings of the AAAI …, 2021 - ojs.aaai.org
Session-based Recommendations recommend the next possible item for the user with
anonymous sessions, whose challenge is that the user's behavioral preference can only be …
anonymous sessions, whose challenge is that the user's behavioral preference can only be …
Collaborative graph learning for session-based recommendation
Session-based recommendation (SBR), which mainly relies on a user's limited interactions
with items to generate recommendations, is a widely investigated task. Existing methods …
with items to generate recommendations, is a widely investigated task. Existing methods …
Denoising and prompt-tuning for multi-behavior recommendation
In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …
Dynamic intent-aware iterative denoising network for session-based recommendation
Session-based recommendation aims to predict items that a user will interact with based on
historical behaviors in anonymous sessions. It has long faced two challenges:(1) the …
historical behaviors in anonymous sessions. It has long faced two challenges:(1) the …
Category-aware multi-relation heterogeneous graph neural networks for session-based recommendation
Session-based recommendation (SBR) is one of the hot research areas in recent years.
Various SBR models have been proposed, of which graph neural network (GNN)-based …
Various SBR models have been proposed, of which graph neural network (GNN)-based …
Multi-faceted global item relation learning for session-based recommendation
As an emerging paradigm, session-based recommendation is aimed at recommending the
next item based on a set of anonymous sessions. Effectively representing a session that is …
next item based on a set of anonymous sessions. Effectively representing a session that is …
Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
Recent years have witnessed the remarkable success of recommendation systems (RSs) in
alleviating the information overload problem. As a new paradigm of RSs, session-based …
alleviating the information overload problem. As a new paradigm of RSs, session-based …