2.4k citations · 7.9k across the 182 of their papers we have counts for
29 papers · 1 filter
ConnectionMind: Leveraging Social Networks and Large Language Models for Personalized Recommendation at Meta
Haoyu Han, Yuming Liu, Lei Huang +3
Modern recommendation systems on social media platforms such as Meta must model complex social relationships, including friendships, group memberships, and creator interactions, al…
Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation
Jingzhe Liu, Hanbing Wang, Jiliang Tang +4
Generative recommendation (GR) is an increasingly popular paradigm in recommender systems, with a prominent line of work using LLMs as autoregressive backbones to predict the next…
An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
Haoyu Han, Li Ma, Hanbing Wang +9
Sequential recommendation has increasingly shifted toward generative recommenders that combine sequential patterns with semantic item information. Yet these methods are often evalu…
Exploring Query Understanding for Amazon Product Search
Chen Luo, Xianfeng Tang, Hanqing Lu +12
Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique chara…
Rethinking Large Language Model Architectures for Sequential Recommendations
Hanbing Wang, Xiaorui Liu, Wenqi Fan +7
Recently, sequential recommendation has been adapted to the LLM paradigm to enjoy the power of LLMs. LLM-based methods usually formulate recommendation information into natural lan…
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
Juanhui Li, Haoyu Han, Zhikai Chen +4
Session-based recommendation has gained increasing attention in recent years, with its aim to offer tailored suggestions based on users' historical behaviors within sessions. To ad…
Embedding in Recommender Systems: A Survey
Maolin Wang, Xinjian Zhao, Wanyu Wang +9
Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that conv…
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Wei Jin, Haitao Mao, Zheng Li +17
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…
Recommender Systems in the Era of Large Language Models (LLMs)
Zihuai Zhao, Wenqi Fan, Jiatong Li +8
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that c…
Multimodal Recommender Systems: A Survey
Qidong Liu, Jiaxi Hu, Yutian Xiao +5
The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier an…
Whole Page Unbiased Learning to Rank
Haitao Mao, Lixin Zou, Yujia Zheng +5
The page presentation biases in the information retrieval system, especially on the click behavior, is a well-known challenge that hinders improving ranking models' performance wit…
Graph Enhanced BERT for Query Understanding
Juanhui Li, Yao Ma, Wei Zeng +4
Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. However, it is inherently challenging since…
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Yiqi Wang, Chaozhuo Li, Zheng Liu +5
Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffe…
Graph Trend Filtering Networks for Recommendations
Wenqi Fan, Xiaorui Liu, Wei Jin +3
Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict ho…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu +6
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao +5
Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention. In fact, many of these state-of-the-a…
Jointly Learning to Recommend and Advertise
Xiangyu Zhao, Xudong Zheng, Xiwang Yang +2
Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recom…
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen +3
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) a…
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang +4
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce…
Recommender Systems with Heterogeneous Side Information
Tianqiao Liu, Zhiwei Wang, Jiliang Tang +3
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneou…
Deep Social Collaborative Filtering
Wenqi Fan, Yao Ma, Dawei Yin +3
Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via t…
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Xiangyu Zhao, Long Xia, Lixin Zou +2
With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a n…
Deep Adversarial Social Recommendation
Wenqi Fan, Tyler Derr, Yao Ma +3
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social netwo…