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Zhenzhong Chen

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10 papers

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TIST Journal 2025 Journal Article

Disentangling User Interest and Geographical Context for POI Recommendations

Wenhui Meng
Jiayi Xie
Jing Yi
Yaochen Zhu
Zhenzhong Chen

POI recommendation plays an important role in many applications, such as mobility prediction and location-based advertisements. Existing POI recommendation methods mainly capture the observed patterns in user visits for recommendations, without a comprehensive consideration of the underlying reasons behind the visits. Therefore, different causes of a visit, i.e., users’ interest and geographical context, are entangled. When the underlying causes change (e.g., when a user moves to a new place), the robustness of the recommendations cannot be guaranteed. To address the above challenges, we propose DUIG, a novel user interest and geographical influences disentanglement framework for POI recommendations. We first design a personalized disentanglement strategy to divide check-ins through geographical influence. Specifically, the colliding effect of causality is leveraged to the divide cause-specific check-ins, such that user interest and geographical influence can be properly disentangled in user and POI embeddings. Through this mechanism, even if the underlying reasons that affect a user’s preference change, intervention can be conducted upon the causes to make recommendations generalized to the new scenario. In addition, a geographical-aware negative sampling strategy is proposed to utilize hard negatives to regularize the embedding and disentanglement in the latent space, where a larger sampling probability is introduced for negative samples containing more geographic information. Extensive experiments on two real-world POI recommendation datasets demonstrate the superior performance of DUIG.

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AAAI Conference 2025 Conference Paper

SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control

Binyuan Huang
Yuqing Wen
Yucheng Zhao
Yaosi Hu
Yingfei Liu
Fan Jia
Weixin Mao
Tiancai Wang

Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data for autonomous driving applications and present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We investigate the impact of scaling up the quantity of generative data on the performance of downstream perception models and find that enhancing data diversity plays a crucial role in effectively scaling generative data production. Therefore, we have developed a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data. Extensive evaluations confirm SubjectDrive's efficacy in generating scalable autonomous driving training data, marking a significant step toward revolutionizing data production methods in this field.

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TIST Journal 2024 Journal Article

Deconfounded Cross-modal Matching for Content-based Micro-video Background Music Recommendation

Jing Yi
Zhenzhong Chen

Object-oriented micro-video background music recommendation is a complicated task where the matching degree between videos and background music is a major issue. However, music selections in user-generated content (UGC) are prone to selection bias caused by historical preferences of uploaders. Since historical preferences are not fully reliable and may reflect obsolete behaviors, over-reliance on them should be avoided as knowledge and interests dynamically evolve. In this article, we propose a Deconfounded Cross-Modal matching model to mitigate such bias. Specifically, uploaders’ personal preferences of music genres are identified as confounders that spuriously correlate music embeddings and background music selections, causing the learned system to over-recommend music from majority groups. To resolve such confounders, backdoor adjustment is utilized to deconfound the spurious correlation between music embeddings and prediction scores. We further utilize Monte Carlo estimator with batch-level average as the approximations to avoid integrating the entire confounder space calculated by the adjustment. Furthermore, we design a teacher–student network to utilize the matching of music videos, which is professionally generated content (PGC) with specialized matching, to better recommend content-matching background music. The PGC data are modeled by a teacher network to guide the matching of uploader-selected UGC data of student network by Kullback–Leibler–based knowledge transfer. Extensive experiments on the TT-150k-genre dataset demonstrate the effectiveness of the proposed method. The code is publicly available on https://github.com/jing-1/DecCM

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TIST Journal 2024 Journal Article

Deep Causal Reasoning for Recommendations

Yaochen Zhu
Jing Yi
Jiayi Xie
Zhenzhong Chen

Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy the confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional treatment space. Therefore, we theoretically demonstrate that controlling user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on both simulated and real-world datasets demonstrate that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/Deep-Deconf.

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AAAI Conference 2020 Conference Paper

Video Frame Interpolation via Deformable Separable Convolution

Xianhang Cheng
Zhenzhong Chen

Learning to synthesize non-existing frames from the original consecutive video frames is a challenging task. Recent kernel-based interpolation methods predict pixels with a single convolution process to replace the dependency of optical ﬂow. However, when scene motion is larger than the predeﬁned kernel size, these methods yield poor results even though they take thousands of neighboring pixels into account. To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. In addition, we show that the kernel-based methods and conventional ﬂow-based methods are speciﬁc instances of the proposed DSepConv. Experimental results demonstrate that our method signiﬁcantly outperforms the other kernel-based interpolation methods and shows strong performance on par or even better than the state-of-the-art algorithms both qualitatively and quantitatively.

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IJCAI Conference 2019 Conference Paper

A Review-Driven Neural Model for Sequential Recommendation

Chenliang Li
Xichuan Niu
Xiangyang Luo
Zhenzhong Chen
Cong Quan

Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering user's intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models.

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AAAI Conference 2019 Conference Paper

Meta Learning for Image Captioning

Nannan Li
Zhenzhong Chen
Shan Liu

Reinforcement learning (RL) has shown its advantages in image captioning by optimizing the non-differentiable metric directly in the reward learning process. However, due to the reward hacking problem in RL, maximizing reward may not lead to better quality of the caption, especially from the aspects of propositional content and distinctiveness. In this work, we propose to use a new learning method, meta learning, to utilize supervision from the ground truth whilst optimizing the reward function in RL. To improve the propositional content and the distinctiveness of the generated captions, the proposed model provides the global optimal solution by taking different gradient steps towards the supervision task and the reinforcement task, simultaneously. Experimental results on MS COCO validate the effectiveness of our approach when compared with the state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition

Haoze Wu
Jiawei Liu
Zheng-Jun Zha
Zhenzhong Chen
Xiaoyan Sun

Recent works use 3D convolutional neural networks to explore spatio-temporal information for human action recognition. However, they either ignore the correlation between spatial and temporal features or suffer from high computational cost by spatio-temporal features extraction. In this work, we propose a novel and efficient Mutually Reinforced Spatio-Temporal Convolutional Tube (MRST) for human action recognition. It decomposes 3D inputs into spatial and temporal representations, mutually enhances both of them by exploiting the interaction of spatial and temporal information and selectively emphasizes informative spatial appearance and temporal motion, meanwhile reducing the complexity of structure. Moreover, we design three types of MRSTs according to the different order of spatial and temporal information enhancement, each of which contains a spatio-temporal decomposition unit, a mutually reinforced unit and a spatio-temporal fusion unit. An end-to-end deep network, MRST-Net, is also proposed based on the MRSTs to better explore spatio-temporal information in human actions. Extensive experiments show MRST-Net yields the best performance, compared to state-of-the-art approaches.

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IJCAI Conference 2018 Conference Paper

Image Cationing with Visual-Semantic LSTM

Nannan Li
Zhenzhong Chen

In this paper, a novel image captioning approach is proposed to describe the content of images. Inspired by the visual processing of our cognitive system, we propose a visual-semantic LSTM model to locate the attention objects with their low-level features in the visual cell, and then successively extract high-level semantic features in the semantic cell. In addition, a state perturbation term is introduced to the word sampling strategy in the REINFORCE based method to explore proper vocabularies in the training process. Experimental results on MS COCO and Flickr30K validate the effectiveness of our approach when compared to the state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

Scanpath Prediction for Visual Attention using IOR-ROI LSTM

Zhenzhong Chen
Wanjie Sun

Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i. e. , an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for predicting the next ROI given the inhibited image features. Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths.

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