2,723 research outputs found
On User Modelling for Personalised News Video Recommendation
In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation
Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization
Peer reviewedPostprin
When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm
User behavior analysis is crucial in human-centered AI applications. In this
field, the collection of sufficient and high-quality user behavior data has
always been a fundamental yet challenging problem. An intuitive idea to address
this problem is automatically simulating the user behaviors. However, due to
the subjective and complex nature of human cognitive processes, reliably
simulating the user behavior is difficult. Recently, large language models
(LLM) have obtained remarkable successes, showing great potential to achieve
human-like intelligence. We argue that these models present significant
opportunities for reliable user simulation, and have the potential to
revolutionize traditional study paradigms in user behavior analysis. In this
paper, we take recommender system as an example to explore the potential of
using LLM for user simulation. Specifically, we regard each user as an
LLM-based autonomous agent, and let different agents freely communicate, behave
and evolve in a virtual simulator called RecAgent. For comprehensively
simulation, we not only consider the behaviors within the recommender system
(\emph{e.g.}, item browsing and clicking), but also accounts for external
influential factors, such as, friend chatting and social advertisement. Our
simulator contains at most 1000 agents, and each agent is composed of a
profiling module, a memory module and an action module, enabling it to behave
consistently, reasonably and reliably. In addition, to more flexibly operate
our simulator, we also design two global functions including real-human playing
and system intervention. To evaluate the effectiveness of our simulator, we
conduct extensive experiments from both agent and system perspectives. In order
to advance this direction, we have released our project at
{https://github.com/RUC-GSAI/YuLan-Rec}.Comment: 26 pages, 9 figure
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