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Found 8 papers, 4 papers with code
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization
Domain generalization faces challenges due to the distribution shift between training and testing sets, and the presence of unseen target domains.
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models
While some methods overcome the need for training by leveraging image modality cache and retrieval, they overlook the text modality's importance and cross-modal cues for the efficient adaptation of parameters in visual-language models.
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning
It is a step-by-step linear reasoning process that adjusts the length of the chain to improve the performance of generated prompts.
Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation
In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL).
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
To facilitate model learning, we further collect rich features of users and items as well as users' behavior history.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.
