Deep learning models for serendipity recommendations: a survey and new perspectives
Serendipitous recommendations have emerged as a compelling approach to deliver users
with unexpected yet valuable information, contributing to heightened user satisfaction and …
with unexpected yet valuable information, contributing to heightened user satisfaction and …
Food recommendation with graph convolutional network
Food recommendation has attracted increasing attentions to various food-related
applications and services. The food recommender models aim to match users' preferences …
applications and services. The food recommender models aim to match users' preferences …
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 …
Understanding and modeling passive-negative feedback for short-video sequential recommendation
Sequential recommendation is one of the most important tasks in recommender systems,
which aims to recommend the next interacted item with historical behaviors as input …
which aims to recommend the next interacted item with historical behaviors as input …
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval systems in order to
satisfy both the various interests of customers and the equal market exposure of providers …
satisfy both the various interests of customers and the equal market exposure of providers …
A contrastive learning-based task adaptation model for few-shot intent recognition
Few-shot intent recognition aims to identify user's intent from the utterance with limited
training data. A considerable number of existing methods mainly rely on the generic …
training data. A considerable number of existing methods mainly rely on the generic …
BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation
Session-based recommendation (SBR) and multi-behavior recommendation (MBR) are both
important problems and have attracted the attention of many researchers and practitioners …
important problems and have attracted the attention of many researchers and practitioners …
Are we really achieving better beyond-accuracy performance in next basket recommendation?
Next basket recommendation (NBR) is a special type of sequential recommendation that is
increasingly receiving attention. So far, most NBR studies have focused on optimizing the …
increasingly receiving attention. So far, most NBR studies have focused on optimizing the …
MC-RGN: Residual Graph Neural Networks based on Markov Chain for sequential recommendation
R Chen, J Fan, M Wu - Information Processing & Management, 2023 - Elsevier
Sequential recommendation aims to predict the next item the user will interact with based on
his/her historical interaction sequences. Existing sequential recommendation methods …
his/her historical interaction sequences. Existing sequential recommendation methods …
Disentangled Multi-interest Representation Learning for Sequential Recommendation
Recently, much effort has been devoted to modeling users' multi-interests (aka multi-faceted
preferences) based on their behaviors, aiming to accurately capture users' complex …
preferences) based on their behaviors, aiming to accurately capture users' complex …