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Found 11 papers, 1 papers with code
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism
In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).
A Deep Behavior Path Matching Network for Click-Through Rate Prediction
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making.
A New Approach to Training Multiple Cooperative Agents for Autonomous Driving
Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge.
Improving Micro-video Recommendation by Controlling Position Bias
Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario.
Improving Micro-video Recommendation via Contrastive Multiple Interests
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.
HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification
Many important classification problems in the real-world consist of a large number of closely related categories in a hierarchical structure or taxonomy.
Multi Label Text Classification
Multi-Label Text Classification
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A Behavior-aware Graph Convolution Network Model for Video Recommendation
Interactions between users and videos are the major data source of performing video recommendation.
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model
To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance.
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models.
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.
A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features
This model captures structural features by a sequential variational autoencoder component and leverages a topic modeling component based on Gaussian distribution to enhance the recognition of text semantics.
