Collaborative filtering (CF) is the basic method for recommendation with implicit feedback. Recently, various state-of-the-art CF integrates graph neural networks. However, they often suffer from popularity bias, causing recommendations to deviate from users' genuine preferences. Additionally, several contrastive learning methods based on the in-batch sample strategy have been proposed to train the CF model effectively, but they are prone to suffering from sample bias. To address this problem, debiased contrastive loss has been employed in the recommendation, but instead of personalized debiasing, it treats each user equally. In this paper, we propose a popularity-aware debiased contrastive loss for CF, which can adaptively correct the positive and negative scores based on the popularity of users and items. Our approach aims to reduce the negative impact of popularity and sample bias simultaneously. We theoretically analyze the effectiveness of the proposed method and reveal the relationship between popularity and gradient, which justifies the correction strategy. We extensively evaluate our method on three public benchmarks over balanced and imbalanced settings. The results demonstrate its superiority over the existing debiased strategies, not only on the entire datasets but also when segmenting the datasets based on item popularity.

Self-attention, which allows transformers to capture deep bidirectional contexts, plays a vital role in BERT-like pre-trained language models. However, the maximum likelihood pre-training objective of BERT may produce an anisotropic word embedding space, which leads to biased attention scores for high-frequency tokens, as they are very close to each other in representation space and thus have higher similarities. This bias may ultimately affect the encoding of global contextual information. To address this issue, we propose TOCOL, a TOken-Level COntrastive Learning framework for improving the contextual representation of pre-trained language models, which integrates a novel self-supervised objective to the attention mechanism to reshape the word representation space and encourages PLM to capture the global semantics of sentences. Results on the GLUE Benchmark show that TOCOL brings considerable improvement over the original BERT. Furthermore, we conduct a detailed analysis and demonstrate the robustness of our approach for low-resource scenarios.

Personalized recommendation plays a central role in various online applications. To provide quality recommendation service, it is of crucial importance to consider multi-modal information associated with users and items, e.g., review text, description text, and images. However, many existing approaches do not fully explore and fuse multiple modalities. To address this problem, we propose a multi-modal contrastive pre-training model for recommendation. We first construct a homogeneous item graph and a user graph based on the relationship of co-interaction. For users, we propose intra-modal aggregation and inter-modal aggregation to fuse review texts and the structural information of the user graph. For items, we consider three modalities: description text, images, and item graph. Moreover, the description text and image complement each other for the same item. One of them can be used as promising supervision for the other. Therefore, to capture this signal and better exploit the potential correlation of intra-modalities, we propose a self-supervised contrastive inter-modal alignment task to make the textual and visual modalities as similar as possible. Then, we apply inter-modal aggregation to obtain the multi-modal representation of items. Next, we employ a binary cross-entropy loss function to capture the potential correlation between users and items. Finally, we fine-tune the pre-trained multi-modal representations using an existing recommendation model. We have performed extensive experiments on three real-world datasets. Experimental results verify the rationality and effectiveness of the proposed method.

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations.

Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challenges especially from the side of finance, such as the balance of risk and return, the resistance to extreme loss, and the interpretability of strategies, which limit the application of DL-based strategies in real-life financial markets. In this work, we propose AlphaStock, a novel reinforcement learning (RL) based investment strategy enhanced by interpretable deep attention networks, to address the above challenges. Our main contributions are summarized as follows: i) We integrate deep attention networks with a Sharpe ratio-oriented reinforcement learning framework to achieve a risk-return balanced investment strategy; ii) We suggest modeling interrelationships among assets to avoid selection bias and develop a cross-asset attention mechanism; iii) To our best knowledge, this work is among the first to offer an interpretable investment strategy using deep reinforcement learning models. The experiments on long-periodic U.S. and Chinese markets demonstrate the effectiveness and robustness of AlphaStock over diverse market states. It turns out that AlphaStock tends to select the stocks as winners with high long-term growth, low volatility, high intrinsic value, and being undervalued recently.

Recent years have witnessed the continuous growth of megalopolises worldwide, which makes urban safety a top priority in modern city life. Among various threats, dangerous goods such as gas and hazardous chemicals transported through and around cities have increasingly become the deadly "bomb" we sleep with every day. In both academia and government, tremendous efforts have been dedicated to dealing with dangerous goods transportation (DGT) issues, but further study is still in great need to quantify the problem and explore its intrinsic dynamics in a big data perspective. In this paper, we present a novel system called DGeye, which features a "duet" between DGT trajectory data and human mobility data for risky zones identification. Moreover, DGeye innovatively takes risky patterns as the keystones in DGT management, and builds causality networks among them for pain point identification, attribution and prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye. In particular, the report generated by DGeye driven the Beijing government to lay down gas pipelines for the famous Guijie food street.

Traffic speed prediction is a long-standing and critically important topic in the area of Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. Traffic speed prediction, however, is still in its initial stage without making full use of spatio-temporal traffic information. In light of this, in this paper, we propose a deep learning method with an Error-feedback Recurrent Convolutional Neural Network structure (eRCNN) for continuous traffic speed prediction. By integrating the spatio-temporal traffic speeds of contiguous road segments as an input matrix, eRCNN explicitly leverages the implicit correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback neurons to the recurrent layer, eRCNN learns from prediction errors so as to meet predictive challenges rising from abrupt traffic events such as morning peaks and traffic accidents. Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the strong predictive power of eRCNN in comparison to some state-of-the-art competitors. The necessity of weight pre-training using a transfer learning notion has also been testified. More interestingly, we design a novel influence function based on the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing.

In this paper, we propose a novel unsupervised color spatial pyramid matching (UCSPM) approach for person re-identification. It is well motivated by our study on spatial pyramid to build effective structural object representation for person re-identification. Through the combination of illumination invariance color feature, UCSPM can well cope with the variations of viewpoint, illumination and pose. First, local superpixel regions are divided to accurately represent the color feature. Second, human body are divided into increasing fine vertical sub-regions to construct the spatial pyramid matching scheme. Third, the color feature and its spatial distribution information are used in a pyramid match kernel for calculating the similarity between person and person. The effectiveness of our approach is validated on the VIPeR dataset and CUHK campus dataset. Comparing with other approaches, our UCSPM improves the best unsupervised rank-1 matching rate on the VIPeR dataset by 3.08% with only one kind of feature—color.

Currently the collaborative filtering based recommender system has become more and more indispensable due to its capability in providing users with personalised suggestions. Despite its advances in term of efficiency, easy implementation and robustness, traditional collaborative filtering techniques suffer from several challenges such as cold-start and data sparsity. To overcome these limitations, external information is expected to help improve the overall effectiveness. Among the diverse context information, trust relationships is a widely utilised mechanism. Meanwhile, researchers also found distrust relationships is unavoidable in social network and recommender systems can benefit from distrust information. However, most existed distrusted oriented methods do not take the property of multi-facets in distrust relationships into consideration. In this paper, we exploit distrust relationships in a multi-faceted perspective and proposed a matrix factorization based model with integration of different distrust relationship of quality user between different people. Experimental study on well-known dataset has shown promising result and it is expected that this work could provide insight for researchers in this domain to further discuss the distrust in recommender systems.