Educational recommender systems have become a necessity in the recent years due to overload of available educational resource which makes it difficult for an individual to manually hunt for the required resource on the internet. E-learning recommender systems simplify the tedious task of gathering the right web pages and web documents from the scattered world wide web repositories according to every users' requirements thus increasing the demand and hence the curiosity to study them. Retrieval of a handful of recommendations from a very huge collection of web pages using different recommendation techniques becomes a productive and time efficient process when the system functions with a set of cooperative agents. The system is also required to keep up with the changing user interests and web resources in the dynamic web environment, and hence adaptivity is an important factor in determining the efficiency of recommender systems. The paper provides an overview of such adaptive multi-agent e-learning recommender systems and the concepts employed to implement them. It precisely provides all the information required by a researcher who wants to study the state-of-the-art work on such systems thus enabling him to decide on the implementation concepts for his own system.
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
National University of Singapore（新加坡国立大学）；Polytechnic University of Bari（巴里理工大学）；Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）
University of Minnesota（明尼苏达大学）；University of Klagenfurt（克拉根福大学）；Indiana University（印第安纳大学）
Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users. In reality, however, various important and interesting phenomena only emerge or become visible over time, e.g., when a recommender system continuously reinforces the popularity of already successful artists on a music streaming site or when recommendations that aim at profit maximization lead to a loss of consumer trust in the long run. In this paper, we discuss how Agent-Based Modeling and Si…
AMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders
Center for AI Research, VinUniversity（越南 Vin 大学人工智能研究中心）；College of Engineering and Computer Science, VinUniversity（越南 Vin 大学工程与计算机科学学院）；Computer Science, VinUniversity（越南 Vin 大学计算机科学）
RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
Meituan（美团）；Fudan University（复旦大学）；Nankai University（南开大学）；North China University of Technology（华北理工大学）；University of Science and Technology of China（中国科学技术大学）
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）
In this position paper, we discuss the merits of simulating privacy dynamics in recommender systems. We study this issue at hand from two perspectives: Firstly, we present a conceptual approach to integrate privacy into recommender system simulations, whose key elements are privacy agents. These agents can enhance users' profiles with different privacy preferences, e.g., their inclination to disclose data to the recommender system. Plus, they can protect users' privacy by guarding all actions that could be a threat to privacy. For example, agents can prohibit a user's privacy-threatening actio…
A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user pref…
Towards Efficient Hypergraph and Multi-LLM Agent Recommender Systems
Research Ireland ML-LABS；Dublin City University（都柏林城市大学）；ADAPT Centre（ADAPT中心）；School of Computing（计算学院）；Insight Research Ireland Centre for Data Analytics（Insight Research Ireland 数据分析中心）；University College Dublin（都柏林大学）
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the model incrementally using only the newly arrived data. However, this may impede the model's ability to retain long-term information due to the potential overfitting and forgetting issues. To address this problem, we propose a novel Adaptive Sequential Model Generation (ASMG) framework, which generates a better serving model from a sequence of historical models via a meta generator. For the design of the meta generato…
The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the …
MARC: Multimodal and Multi-Task Agentic Retrieval-Augmented Generation for Cold-Start Recommender System
Department of Industrial Data Engineering, Hanyang University, Republic of Korea（工业数据工程系，翰阳大学）；School of Interdisciplinary Industrial Studies, Hanyang University, Republic of Korea（跨学科工业研究学院，翰阳大学）
Traditional robust recommendation methods view atypical user-item interactions as noise and aim to reduce their impact with some kind of noise filtering technique, which often suffers from two challenges. First, in real world, atypical interactions may signal users' temporary interest different from their general preference. Therefore, simply filtering out the atypical interactions as noise may be inappropriate and degrade the personalization of recommendations. Second, it is hard to acquire the temporary interest since there are no explicit supervision signals to indicate whether an interacti…
iAgent: LLM Agent as a Shield between User and Recommender Systems
Rutgers University（罗格斯大学）；University of Technology Sydney（悉尼技术大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Nanyang Technological University（南洋理工大学）
Traditional industrial recommenders are usually trained on a single business domain and then serve for this domain. However, in large commercial platforms, it is often the case that the recommenders need to make click-through rate (CTR) predictions for multiple business domains. Different domains have overlapping user groups and items. Thus, there exist commonalities. Since the specific user groups have disparity and the user behaviors may change in various business domains, there also have distinctions. The distinctions result in domain-specific data distributions, making it hard for a single…
In this paper, we study the problem of modeling users' diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model users' various interests, we propose a Memory Attention-aware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep \textit{adaptive user representations}. Trained in an end-to-end fashion, MARS adaptively summarizes users' interests. In the experiments, MARS outperforms seven state-of-the-art methods on three real-world …
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years. Developing and training models and algorithms for such recommenders can be especially difficult using static datasets, which often fail to offer the types of counterfactual predictions needed to evaluate policies over extended horizons. To address this, we develop RecSim NG, a probabilistic platform for the simulation of multi-agent recommende…
