Yongfeng Zhang

Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification

2015 · arXiv (Cornell University)

Sentiment analysis on user reviews helps to keep track of user reactions towards products, and make advices to users about what to buy. State-of-the-art review-level sentiment classification techniques could give pretty good precisions of above …

SIGIR 2019 Tutorial on Explainable Recommendation and Search

2019

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help to …

Personalized Counterfactual Fairness in Recommendation

2021 · arXiv (Cornell University)

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness …

Towards Personalized Fairness based on Causal Notion

2021

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness …

Counterfactual Review-based Recommendation

2021

Incorporating review information into the recommender system has been demonstrated to be an effective method for boosting the recommendation performance. Previous research mainly focus on designing advanced architectures to better profile the users and items. …

Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems

2022 · arXiv (Cornell University)

As recommender systems become increasingly sophisticated and complex, they often suffer from lack of fairness and transparency. Providing robust and unbiased explanations for recommendations has been drawing more and more attention as it can help …

Deconfounded Causal Collaborative Filtering

2021 · arXiv (Cornell University)

Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each specific model …

Disentangling Memory and Reasoning Ability in Large Language Models

2024 · arXiv (Cornell University)

Large Language Models (LLMs) have demonstrated strong performance in handling complex tasks requiring both extensive knowledge and reasoning abilities. However, the existing LLM inference pipeline operates as an opaque process without explicit separation between knowledge …

LiteCUA: Computer as MCP Server for Computer-Use Agent on AIOS

2025 · arXiv (Cornell University)

We present AIOS 1.0, a novel platform designed to advance computer-use agent (CUA) capabilities through environmental contextualization. While existing approaches primarily focus on building more powerful agent frameworks or enhancing agent models, we identify a …

Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation

2015

Previous research on Recommender Systems (RS), especially the continuously popular approach of Collaborative Filtering (CF), has been mostly focusing on the information resource of explicit user numerical ratings or implicit (still numerical) feedbacks. However, the …

Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis

2015

The frequently changing user preferences and/or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user/item records when splitting …

Learning to Rank Features for Recommendation over Multiple Categories

2016

Incorporating phrase-level sentiment analysis on users' textual reviews for recommendation has became a popular meth-od due to its explainable property for latent features and high prediction accuracy. However, the inherent limitations of the existing model …

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

2017

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems …

Sequential Recommendation with User Memory Networks

2018

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches …

Explainable Recommendation: A Survey and New Perspectives

2020 · Foundations and Trends® in Information Retrieval

Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model …

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

2018 · Algorithms

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms—especially the collaborative filtering (CF)- based approaches with shallow or deep models—usually work with various unstructured information sources for recommendation, such as …

Towards Conversational Search and Recommendation

2018

Conversational search and recommendation based on user-system dialogs exhibit major differences from conventional search and recommendation tasks in that 1) the user and system can interact for multiple semantically coherent rounds on a task through …

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

2019

Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, …

Relational Collaborative Filtering

2019

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world …

A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation

2019

Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single …

Personalized re-ranking for recommendation

2019

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which …

Efficient Neural Matrix Factorization without Sampling for Recommendation

2020 · ACM Transactions on Information Systems

Recommendation systems play a vital role to keep users engaged with personalized contents in modern online platforms. Recently, deep learning has revolutionized many research fields and there is a surge of interest in applying it …

CAFE

2020

Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to generate explanations of why particular decisions are made. This can be achieved by …

Generate Neural Template Explanations for Recommendation

2020

Personalized recommender systems are important to assist user decision-making in the era of information overload. Meanwhile, explanations of the recommendations further help users to better understand the recommended items so as to make informed choices, …

Towards Long-term Fairness in Recommendation

2021

As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been …

User-oriented Fairness in Recommendation

2021

As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important …

Counterfactual Data-Augmented Sequential Recommendation

2021

Sequential recommendation aims at predicting users' preferences based on their historical behaviors. However, this recommendation strategy may not perform well in practice due to the sparsity of the real-world data. In this paper, we propose …

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

2022

For a long time, different recommendation tasks require designing task-specific architectures and training objectives. As a result, it is hard to transfer the knowledge and representations from one task to another, thus restricting the generalization …