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Found 21 papers, 9 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.
NetSafe: Exploring the Topological Safety of Multi-agent Networks
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry.
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment
Our finding demonstrates that LLM%u2019s judgments, similar to human judgments, are also influenced by threshold priming biases, and suggests that researchers and system engineers should take into account potential human-like cognitive biases in designing, evaluating, and auditing LLMs in IR tasks and beyond.
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM
In this paper, we propose to streamline the semantic tokenization and generative recommendation process with a unified framework, dubbed STORE, which leverages a single large language model (LLM) for both tasks.
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference challenges on large-scale graphs.
Vector Quantization for Recommender Systems: A Review and Outlook
Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems.
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation
Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems.
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests.
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 BiDiffuser 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), Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge modalities, EasyGen leverages BiDiffuser, a bidirectional conditional diffusion model, to foster more efficient modality interactions.
Dataset Condensation for Recommendation
Specifically, we model the discrete user-item interactions via a probabilistic approach and design a pre-augmentation module to incorporate the potential preferences of users into the condensed datasets.
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.
