733 citations · 3.6k across the 42 of their papers we have counts for
37 papers · 1 filter
Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
Lei Li, Yongfeng Zhang, Dugang Liu +1
Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender…
A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News
Xinyi Li, Yongfeng Zhang, Edward C. Malthouse
Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language m…
Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT
Guo Lin, Yongfeng Zhang
This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises…
PBNR: Prompt-based News Recommender System
Xinyi Li, Yongfeng Zhang, Edward C. Malthouse
Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching…
Exploration and Regularization of the Latent Action Space in Recommendation
Shuchang Liu, Qingpeng Cai, Bowen Sun +7
In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…
Variation Control and Evaluation for Generative SlateRecommendations
Shuchang Liu, Fei Sun, Yingqiang Ge +2
Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations. In o…
EXTRA: Explanation Ranking Datasets for Explainable Recommendation
Lei Li, Yongfeng Zhang, Li Chen
Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, thei…
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8
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 appro…
Generate Natural Language Explanations for Recommendation
Hanxiong Chen, Xu Chen, Shaoyun Shi +1
Providing personalized explanations for recommendations can help users to understand the underlying insight of the recommendation results, which is helpful to the effectiveness, tr…
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Yikun Xian, Zuohui Fu, Handong Zhao +8
Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to gen…
COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
Zuohui Fu, Yikun Xian, Yaxin Zhu +2
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE. The dataset is constructed from an Amazon revi…
E-commerce Recommendation with Weighted Expected Utility
Zhichao Xu, Yi Han, Yongfeng Zhang +1
Different from shopping at retail stores, consumers on e-commerce platforms usually cannot touch or try products before purchasing, which means that they have to make decisions whe…
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
Yikun Xian, Zuohui Fu, Qiaoying Huang +2
Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide expla…
Learning Personalized Risk Preferences for Recommendation
Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3
The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…
Understanding Echo Chambers in E-commerce Recommender Systems
Yingqiang Ge, Shuya Zhao, Honglu Zhou +4
Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper item…
Fairness-Aware Explainable Recommendation over Knowledge Graphs
Zuohui Fu, Yikun Xian, Ruoyuan Gao +8
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in parti…
Learning Post-Hoc Causal Explanations for Recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu +3
State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of blac…
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
Liu Yang, Minghui Qiu, Chen Qu +5
Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarifi…
Explainable Product Search with a Dynamic Relation Embedding Model
Qingyao Ai, Yongfeng Zhang, Keping Bi +1
Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models…
Conversational Product Search Based on Negative Feedback
Keping Bi, Qingyao Ai, Yongfeng Zhang +1
Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. Dur…
Attentive History Selection for Conversational Question Answering
Chen Qu, Liu Yang, Minghui Qiu +4
Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. One of its major challenges is to leverage the conversation history to und…
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Yikun Xian, Zuohui Fu, S. Muthukrishnan +2
Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing app…
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Xin Xin, Xiangnan He, Yongfeng Zhang +2
Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real…
BERT with History Answer Embedding for Conversational Question Answering
Chen Qu, Liu Yang, Minghui Qiu +3
Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation hist…