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Found 10 papers, 4 papers with code
Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks
Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.
Disentangled Contrastive Collaborative Filtering
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).
Whole Page Unbiased Learning to Rank
To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model with causal discovery and mitigate the biases induced by multiple SERP features with no specific design.
Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation
Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications.
Hypergraph Contrastive Collaborative Filtering
Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination.
Sequential Recommendation with User Evolving Preference Decomposition
To solve this problem, in this paper, we propose a novel sequential recommender model via decomposing and modeling user independent preferences.
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.
User behavior understanding in real world settings
It is necessary to learn a dynamic group of representations according the item groups in a user historical behavior.
Gumble Softmax For User Behavior Modeling
We propose a sequential model with dynamic number of representations for recommendation systems (RDRSR).
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
