Ruiming Tang

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

2017 · arXiv (Cornell University)

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature …

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

2022 · arXiv (Cornell University)

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation that leverages weight-sharing and SGD where all candidate operations are trained simultaneously. …

A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

2020

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize …

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

2021 · arXiv (Cornell University)

Recommender systems are often asked to serve multiple recommendation scenarios or domains. Fine-tuning a pre-trained CTR model from source domains and adapting it to a target domain allows knowledge transferring. However, optimizing all the parameters …

Retrieval & Interaction Machine for Tabular Data Prediction

2021 · arXiv (Cornell University)

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a …

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

2021

Effectively modeling feature interactions is crucial for CTR prediction in industrial recommender systems. The state-of-the-art deep CTR models with parallel structure (e.g., DCN) learn explicit and implicit feature interactions through independent parallel networks. However, these …

An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

2022 · arXiv (Cornell University)

To provide click simulation or relevance estimation based on users' implicit interaction feedback, click models have been much studied during recent years. Most click models focus on user behaviors towards a single list. However, with …

A Brief History of Recommender Systems

2022 · arXiv (Cornell University)

Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry. Currently, recommender system has become one of the most successful …

A Bird's-eye View of Reranking: from List Level to Page Level

2022 · arXiv (Cornell University)

Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list …

Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation

2022 · arXiv (Cornell University)

Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to embed the latent features by a compact sequence of discrete numbers, recently …

Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

2023 · arXiv (Cornell University)

With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning …

Diffusion Augmentation for Sequential Recommendation

2023

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, …

Learning to Edit: Aligning LLMs with Knowledge Editing

2024 · arXiv (Cornell University)

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing …

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations

2024 · arXiv (Cornell University)

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as …

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations

2025

Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall short in recommender systems. This limitation arises from their difficulty in …

From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs

2025 · arXiv (Cornell University)

Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation for growth and effective interaction with the world. It …

Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to …

Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data

2018 · ACM Transactions on Information Systems

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and …

AutoFIS

2020

Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large …