Yiqun Liu

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 …

Detecting Crowdturfing "Add to Favorites" Activities in Online Shopping

2018

"Add to Favorites" is a popular function in online shopping sites which helps users to make a record of potentially interesting items for future purchases. It is usually regarded as a type of explicit feedback …

A Two-Stage Model for User's Examination Behavior in Mobile Search

2018

With the rapid growth of mobile search, it is important to understand how users browse the mobile SERPs and allocate their limited attention to each result. To address this problem, we introduce a two-stage examination …

Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation

2018

Nowadays, recommender systems provide essential web services on the Internet. There are mainly two categories of traditional recommendation algorithms: Content-Based (CB) and Collaborative Filtering (CF). CF methods make recommendations mainly according to the historical feedback …

Standing in Your Shoes: External Assessments for Personalized Recommender Systems

2021

The evaluation of recommender systems relies on user preference data, which is difficult to acquire directly because of its subjective nature. Current recommender systems widely utilize users' historical interactions as implicit or explicit feedback, but …

Global or Local: Constructing Personalized Click Models for Web Search

2022 · Proceedings of the ACM Web Conference 2022

Click models are widely used for user simulation, relevance inference, and evaluation in Web search. Most existing click models implicitly assume that users’ relevance judgment and behavior patterns are homogeneous. However, previous studies have shown …

External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

2025 · arXiv (Cornell University)

Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a …

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 …

How good your recommender system is? A survey on evaluations in recommendation

2017 · International Journal of Machine Learning and Cybernetics

Recommender Systems have become a very useful tool for a large variety of domains. Researchers have been attempting to improve their algorithms in order to issue better predictions to the users. However, one of the …

Neural Attentional Rating Regression with Review-level Explanations

2018

Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews hurt model's performance, and are also less meaningful for user's …

Social Attentional Memory Network

2019

Social connections are known to be helpful for modeling users' potential preferences and improving the performance of recommender systems. However, in social-aware recommendations, there are two issues which influence the inference of users' preferences, and …

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

2019

Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based …

An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation

2019

Many previous studies attempt to utilize information from other domains to achieve better performance of recommendation. Recently, social information has been shown effective in improving recommendation results with transfer learning frameworks, and the transfer part …

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 …

A Survey on the Fairness of Recommender Systems

2022 · ACM Transactions on Information Systems

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is …