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Yuanyuan Jin

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2 papers

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IJCAI Conference 2025 Conference Paper

HLMTrans: A Sim-to-Real Transfer Framework for Spatial Crowdsourcing with Human-Guided Language Models

Qingshun Wu
Yafei Li
Lulu Li
Yuanyuan Jin
Shuo He
Mingliang Xu

Reinforcement Learning (RL), trained via trial and error in simulators, has been proven to be an effective approach for addressing task assignment problems in spatial crowdsourcing. However, a performance gap still exists when transferring the simulator-trained RL Models (RLMs) to real-world settings due to the misalignment of travel time. Existing works mostly focus on using data-driven and learning-based methods to predict travel time; unfortunately, these approaches are limited in achieving accurate predictions by requiring a large amount of real-world data covering the entire state distribution. In this paper, we propose a Sim-to-Real Transfer with Human-guided Language Models framework called HLMTrans, which comprises three core modules: RLMs decision for task assignment, sim-to-real transfer with Large Language Models (LLMs), and preference learning from human feedback. HLMTrans first leverages the zero-shot chain-of-thought reasoning capability of LLMs to estimate travel time by capturing the real-world dynamics. This estimation is then input as domain knowledge into the forward model of Grounded Action Transformation (GAT) to enhance the action transformation of RLMs. Further, we design a human preference learning mechanism to fine-tune LLMs, improving their generation quality and enabling RLMs learn a more realistic policy. We evaluate the proposed HLMTrans on two real-world datasets, and the experimental results demonstrate that HLMTrans outperforms the SOTA methods in terms of effectiveness and efficiency.

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AAAI Conference 2018 Conference Paper

Personalized Time-Aware Tag Recommendation

Keqiang Wang
Yuanyuan Jin
Haofen Wang
Hongwei Peng
Xiaoling Wang

Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensor factorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which can not ﬁnd users’ potential interests. In this paper, we propose an uniﬁed tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weights to user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative ﬁltering (CF) to timeaware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time-aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efﬁciency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

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