126 citations · 337 across the 26 of their papers we have counts for
19 papers · 1 filter
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Junjie Huang, Jiarui Qin, Yong Yu +1
Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information bey…
Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
Junjie Huang, Jiarui Qin, Jianghao Lin +3
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly…
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Bo Chen, Xinyi Dai, Huifeng Guo +9
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large lan…
A Comprehensive Survey on Retrieval Methods in Recommender Systems
Junjie Huang, Jizheng Chen, Jianghao Lin +4
In an era dominated by information overload, effective recommender systems are essential for managing the deluge of data across digital platforms. Multi-stage cascade ranking syste…
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Jiachen Zhu, Yichao Wang, Jianghao Lin +4
We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions…
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
Huanshuo Liu, Bo Chen, Menghui Zhu +5
Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However…
D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
Jiarui Qin, Weiwen Liu, Ruiming Tang +2
A vast amount of user behavior data is constantly accumulating on today's large recommendation platforms, recording users' various interests and tastes. Preserving knowledge from t…
A Survey on User Behavior Modeling in Recommender Systems
Zhicheng He, Weiwen Liu, Wei Guo +4
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and…
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Hengyu Zhang, Enming Yuan, Wei Guo +6
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing beha…
Neural Re-ranking in Multi-stage Recommender Systems: A Review
Weiwen Liu, Yunjia Xi, Jiarui Qin +5
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby pla…
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
Wei Guo, Can Zhang, Zhicheng He +6
CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on c…
Retrieval & Interaction Machine for Tabular Data Prediction
Jiarui Qin, Weinan Zhang, Rong Su +5
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 structu…
Deep Learning for Click-Through Rate Estimation
Weinan Zhang, Jiarui Qin, Wei Guo +2
Click-through rate (CTR) estimation plays as a core function module in various personalized online services, including online advertising, recommender systems, and web search etc.…
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Jiarui Jin, Kounianhua Du, Weinan Zhang +5
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path rea…
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
Jiarui Jin, Jiarui Qin, Yuchen Fang +5
There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich sema…
User Behavior Retrieval for Click-Through Rate Prediction
Jiarui Qin, Weinan Zhang, Xin Wu +3
Click-through rate (CTR) prediction plays a key role in modern online personalization services. In practice, it is necessary to capture user's drifting interests by modeling sequen…
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
Jiarui Qin, Kan Ren, Yuchen Fang +2
Sequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resource…
Deep Landscape Forecasting for Real-time Bidding Advertising
Kan Ren, Jiarui Qin, Lei Zheng +3
The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as…
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
Kan Ren, Jiarui Qin, Yuchen Fang +8
User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulate…