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Jiajie Chen

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3 papers

2 author rows

AAAI Conference 2025 Conference Paper

Enhancing Long-and Short-Term Representations for Next POI Recommendations via Frequency and Hierarchical Contrastive Learning

Jiajie Chen
Yu Sang
Peng-Fei Zhang
Jiaan Wang
Jianfeng Qu
Zhixu Li

Next POI recommendation aids users in predicting their destinations of interest and plays an increasingly vital role in location-based social services. Recent works focus on analyzing both long-term and short-term interests in POI recommendation to gain a deeper understanding of user profiles. However, these methods for modeling long-term user’s sequences primarily rely on the Transformer model, which functions as a low-pass filter, often leading to the loss of high-frequency information. Additionally, long-term and short-term sequences are typically modeled independently, with short-term sequences often defined solely by the most recent check-ins, overlooking their interactions and dependencies. Therefore, we propose Enhancing Long-and Short-Term Representations for Next POI Recommendations via Frequency and Hierarchical Contrastive Learning (FHCRec). FHCRec captures both high-frequency and low-frequency information in long-term sequences to model richer long-term user’s preference representations. Moreover, it harnesses the characteristics of the short-term subsequences embedded within long-term sequences to enhance short-term preference characterization via local and global hierarchical contrastive learning, resulting in more personalized short-term preferences. The enhanced long-term and short-term preferences are integrated to improve model recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method.

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ICLR Conference 2021 Conference Paper

Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective

Helong Zhou
Liangchen Song
Jiajie Chen
Ye Zhou
Guoli Wang 0004
Junsong Yuan 0001
Qian Zhang 0009

Knowledge distillation is an effective approach to leverage a well-trained network or an ensemble of them, named as the teacher, to guide the training of a student network. The outputs from the teacher network are used as soft labels for supervising the training of a new network. Recent studies (M ̈uller et al., 2019; Yuan et al., 2020) revealed an intriguing property of the soft labels that making labels soft serves as a good regularization to the student network. From the perspective of statistical learning, regularization aims to reduce the variance, however how bias and variance change is not clear for training with soft labels. In this paper, we investigate the bias-variance tradeoff brought by distillation with soft labels. Specifically, we observe that during training the bias-variance tradeoff varies sample-wisely. Further, under the same distillation temperature setting, we observe that the distillation performance is negatively associated with the number of some specific samples, which are named as regularization samples since these samples lead to bias increasing and variance decreasing. Nevertheless, we empirically find that completely filtering out regularization samples also deteriorates distillation performance. Our discoveries inspired us to propose the novel weighted soft labels to help the network adaptively handle the sample-wise bias-variance tradeoff. Experiments on standard evaluation benchmarks validate the effectiveness of our method. Our code is available in the supplementary.

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IS Journal 2016 Journal Article

Combining Region-of-Interest Extraction and Image Enhancement for Nighttime Vehicle Detection

Hulin Kuang
Long Chen
Feng Gu
Jiajie Chen
Leanne Chan
Hong Yan

In nighttime images, vehicle detection is a challenging task because of low contrast and luminosity. In this article, the authors combine a novel region-of-interest (ROI) extraction approach that fuses vehicle light detection and object proposals together with a nighttime image enhancement approach based on improved multiscale retinex to extract accurate ROIs and enhance images for accurate nighttime vehicle detection. Experimental results demonstrate that the proposed nighttime image enhancement method, score-level multifeature fusion, and the ROI extraction method are all effective for nighttime vehicle detection. But the proposed vehicle detection method demonstrates 93. 34 percent detection rate and outperforms other models, detecting blurred and partly occluded vehicles, as well as vehicles in a variety of sizes, numbers, locations, and backgrounds.

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