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Found 15 papers, 6 papers with code
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation
We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial application.
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
The recent advancements in pretrained multimodal models offer new opportunities and challenges in developing content-aware recommender systems.
Discrete Semantic Tokenization for Deep CTR Prediction
Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.
Benchmarking News Recommendation in the Era of Green AI
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems.
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision
Specifically, we introduce a novel method that enhances the learning of embeddings in SR through the supervision of modality correlations.
TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation
Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset.
EasyGen: Easing Multimodal Generation with a Bidirectional Conditional Diffusion Model and LLMs
We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs).
Dataset Condensation for Recommendation
However, applying existing approaches to condense recommendation datasets is impractical due to following challenges: (i) sampling-based methods are inadequate in addressing the long-tailed distribution problem; (ii) synthesizing-based methods are not applicable due to discreteness of interactions and large size of recommendation datasets; (iii) neither of them fail to address the specific issue in recommendation of false negative items, where items with potential user interest are incorrectly sampled as negatives owing to insufficient exposure.
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
To model the compatibility between user intents and item properties, we design the user-item co-clustering module, maximizing the mutual information of co-clusters of users and items.
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Category information plays a crucial role in enhancing the quality and personalization of recommender systems.
Only Encode Once: Making Content-based News Recommender Greener
Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content.
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services.
Continual Graph Convolutional Network for Text Classification
Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification.
FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation
Item list continuation is proposed to model the overall trend of a list and predict subsequent items.
Weak Supervision Enhanced Generative Network for Question Generation
More specifically, we devise a discriminator, Relation Guider, to capture the relations between the whole passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system.
