A survey on session-based recommender systems
Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …
consumption, services, and decision-making in the overloaded information era and digitized …
Hypergraph contrastive collaborative filtering
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …
users and items into latent representation space, with their correlative patterns from …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
[PDF][PDF] Graph contextualized self-attention network for session-based recommendation.
Session-based recommendation, which aims to predict the user's immediate next action
based on anonymous sessions, is a key task in many online services (eg, e-commerce …
based on anonymous sessions, is a key task in many online services (eg, e-commerce …
STAMP: short-term attention/memory priority model for session-based recommendation
Predicting users' actions based on anonymous sessions is a challenging problem in web-
based behavioral modeling research, mainly due to the uncertainty of user behavior and the …
based behavioral modeling research, mainly due to the uncertainty of user behavior and the …
Graph meta network for multi-behavior recommendation
Modern recommender systems often embed users and items into low-dimensional latent
representations, based on their observed interactions. In practical recommendation …
representations, based on their observed interactions. In practical recommendation …
Neural attentive session-based recommendation
Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short sessions …
recommendation is proposed to generate recommendation results from short sessions …
Multi-behavior recommendation with graph convolutional networks
Traditional recommendation models that usually utilize only one type of user-item interaction
are faced with serious data sparsity or cold start issues. Multi-behavior recommendation …
are faced with serious data sparsity or cold start issues. Multi-behavior recommendation …
Interactive path reasoning on graph for conversational recommendation
Traditional recommendation systems estimate user preference on items from past interaction
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …
Graph-refined convolutional network for multimedia recommendation with implicit feedback
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …
applications of graph convolutional networks (GCNs) in recommendation tasks. In the …