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Found 13 papers, 3 papers with code
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects.
Contrastive Self-supervised Learning in Recommender Systems: A Survey
We then introduce a taxonomy based on the key components of the framework, including view generation strategy, contrastive task, and contrastive objective.
Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis
An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed.
Deep Meta-learning in Recommendation Systems: A Survey
For each recommendation scenario, we further discuss technical details about how existing methods apply meta-learning to improve the generalization ability of recommendation models.
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
To overcome the above issue, this paper advises a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city.
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks.
Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks
In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem.
Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation
First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively.
ALCNN: Attention-based Model for Fine-grained Demand Inference of Dock-less Shared Bike in New Cities
In this paper, we study the problem of inferring fine-grained bike demands anywhere in a new city before the deployment of bikes.
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation.
A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology.
