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Found 13 papers, 3 papers with code
Geo-BERT Pre-training Model for Query Rewriting in POI Search
Finally, we train a BERT-like pre-training model with text and POIs’ graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search.
Text2Bundle: Towards Personalized Query-based Bundle Generation
Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms.
Towards Multi-Subsession Conversational Recommendation
Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where user would still resort to CRS after several subsessions and might preserve vague interests, and system would proactively ask attributes to activate user interests in the current subsession.
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia.
Data-Augmented Counterfactual Learning for Bundle Recommendation
In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve bundle recommendation.
Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation
Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation.
Temporal aware Multi-Interest Graph Neural Network For Session-based Recommendation
Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences.
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.
Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection
The drowsiness detection system is trained and evaluated on the famous Nation Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94. 46%, which outperforms most existing fatigue detection models.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
