42 citations · 86 across the 65 of their papers we have counts for
26 papers · 1 filter
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
NextAds: Towards Next-generation Personalized Video Advertising
Yiyan Xu, Ruoxuan Xia, Wuqiang Zheng +3
With the rapid growth of online video consumption, video advertising has become increasingly dominant in the digital advertising landscape. Yet diverse users and viewing contexts m…
Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation
Yaxin Gong, Chongming Gao, Chenxiao Fan +6
Large language models (LLMs) have spurred interest in agent-based recommender systems, yet most agentic approaches remain user-centric: items stay passive entities whose exposure i…
Bringing Reasoning to Generative Recommendation Through the Lens of Cascaded Ranking
Xinyu Lin, Pengyuan Liu, Wenjie Wang +5
Generative Recommendation (GR) has become a promising end-to-end approach with high FLOPS utilization for resource-efficient recommendation. Despite the effectiveness, we show that…
MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedback
Shihao Cai, Chongming Gao, Haoyan Liu +4
The powerful reasoning and generative capabilities of large language models (LLMs) have inspired researchers to apply them to reasoning-based recommendation tasks, which require in…
Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning
Wuqiang Zheng, Yiyan Xu, Xinyu Lin +3
With the rapid and continuous increase in academic publications, identifying high-quality research has become an increasingly pressing challenge. While recent methods leveraging La…
Heterogeneous User Modeling for LLM-based Recommendation
Honghui Bao, Wenjie Wang, Xinyu Lin +4
Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation. A key cha…
NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Xiaoyan Zhao, Juntao You, Yang Zhang +5
Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users…
Personalized Generation In Large Model Era: A Survey
Yiyan Xu, Jinghao Zhang, Alireza Salemi +6
In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents t…
Order-agnostic Identifier for Large Language Model-based Generative Recommendation
Xinyu Lin, Haihan Shi, Wenjie Wang +4
Leveraging Large Language Models (LLMs) for generative recommendation has attracted significant research interest, where item tokenization is a critical step. It involves assigning…
Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation
Yi Fang, Wenjie Wang, Yang Zhang +4
Aligning Large Language Models (LLMs) with recommendation tasks represents an emerging paradigm in recommendation domain, exhibiting promising performance overall. However, these a…
Efficient Inference for Large Language Model-based Generative Recommendation
Xinyu Lin, Chaoqun Yang, Wenjie Wang +5
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caus…
Proactive Recommendation in Social Networks: Steering User Interest with Causal Inference
Hang Pan, Shuxian Bi, Wenjie Wang +3
Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by…
Leveraging LLMs for Influence Path Planning in Proactive Recommendation
Mingze Wang, Shuxian Bi, Wenjie Wang +3
Recommender systems are pivotal in Internet social platforms, yet they often cater to users' historical interests, leading to critical issues like echo chambers. To broaden user ho…
Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation
Chengbing Wang, Wentao Shi, Jizhi Zhang +3
Recent work has improved recommendation models remarkably by equipping them with debiasing methods. Due to the unavailability of fully-exposed datasets, most existing approaches re…
Debiased Recommendation with Noisy Feedback
Haoxuan Li, Chunyuan Zheng, Wenjie Wang +3
Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased l…
Learnable Item Tokenization for Generative Recommendation
Wenjie Wang, Honghui Bao, Xinyu Lin +5
Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data in…
A Survey of Generative Search and Recommendation in the Era of Large Language Models
Yongqi Li, Xinyu Lin, Wenjie Wang +6
With the information explosion on the Web, search and recommendation are foundational infrastructures to satisfying users' information needs. As the two sides of the same coin, bot…
Proactive Recommendation with Iterative Preference Guidance
Shuxian Bi, Wenjie Wang, Hang Pan +2
Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user int…
Uplift Modeling for Target User Attacks on Recommender Systems
Wenjie Wang, Changsheng Wang, Fuli Feng +3
Recommender systems are vulnerable to injective attacks, which inject limited fake users into the platforms to manipulate the exposure of target items to all users. In this work, w…
Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang +5
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and…
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
Jinqiu Jin, Sihao Ding, Wenjie Wang +1
Common click-through rate (CTR) prediction recommender models tend to exhibit feature-level bias, which leads to unfair recommendations among item groups and inaccurate recommendat…
Diffusion Models for Generative Outfit Recommendation
Yiyan Xu, Wenjie Wang, Fuli Feng +3
Outfit Recommendation (OR) in the fashion domain has evolved through two stages: Pre-defined Outfit Recommendation and Personalized Outfit Composition. However, both stages are con…
Data-efficient Fine-tuning for LLM-based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li +4
Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost o…