Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Weakly Labeled Action Recognition and Detection

Research in human action recognition strives to develop increasingly generalized methods that are robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendous strides in improving recognition accuracy on ever larger and complex benchmark datasets, comprising realistic actions in the wild videos. Unfortunately, the all-encompassing, dense, global representations that bring about such improvements often benefit from the inherent characteristics, specific to datasets and classes, that do not necessarily reflect knowledge about the entity to be recognized. This results in specific models that perform well within datasets but generalize poorly. Furthermore, training of supervised action recognition and detection methods need several precise spatio-temporal manual annotations to achieve good recognition and detection accuracy. For instance, current deep learning architectures require millions of accurately annotated videos to learn robust action classifiers. However, these annotations are quite difficult to achieve. In the first part of this dissertation, we explore the reasons for poor classifier performance when tested on novel datasets, and quantify the effect of scene backgrounds on action representations and recognition. We attempt to address the problem of recognizing human actions while training and testing on distinct datasets when test videos are neither labeled nor available during training. In this scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. We perform different types of partitioning of the GIST feature space for several datasets and compute measures of background scene complexity, as well as, for the extent to which scenes are helpful in action classification. We then propose a new process to obtain a measure of confidence in each pixel of the video being a foreground region using motion, appearance, and saliency together in a 3D-Markov Random Field (MRF) based framework. We also propose multiple ways to exploit the foreground confidence: to improve bag-of-words vocabulary, histogram representation of a video, and a novel histogram decomposition based representation and kernel. The above-mentioned work provides probability of each pixel being belonging to the actor, however, it does not give the precise spatio-temporal location of the actor. Furthermore, above framework would require precise spatio-temporal manual annotations to train an action detector. However, manual annotations in videos are laborious, require several annotators and contain human biases. Therefore, in the second part of this dissertation, we propose a weakly labeled approach to automatically obtain spatio-temporal annotations of actors in action videos. We first obtain a large number of action proposals in each video. To capture a few most representative action proposals in each video and evade processing thousands of them, we rank them using optical flow and saliency in a 3D-MRF based framework and select a few proposals using MAP based proposal subset selection method. We demonstrate that this ranking preserves the high-quality action proposals. Several such proposals are generated for each video of the same action. Our next challenge is to iteratively select one proposal from each video so that all proposals are globally consistent. We formulate this as Generalized Maximum Clique Graph problem (GMCP) using shape, global and fine-grained similarity of proposals across the videos. The output of our method is the most action representative proposals from each video. Using our method can also annotate multiple instances of the same action in a video can also be annotated. Moreover, action detection experiments using annotations obtained by our method and several baselines demonstrate the superiority of our approach. The above-mentioned annotation method uses multiple videos of the same action. Therefore, in the third part of this dissertation, we tackle the problem of spatio-temporal action localization in a video, without assuming the availability of multiple videos or any prior annotations. The action is localized by employing images downloaded from the Internet using action label. Given web images, we first dampen image noise using random walk and evade distracting backgrounds within images using image action proposals. Then, given a video, we generate multiple spatio-temporal action proposals. We suppress camera and background generated proposals by exploiting optical flow gradients within proposals. To obtain the most action representative proposals, we propose to reconstruct action proposals in the video by leveraging the action proposals in images. Moreover, we preserve the temporal smoothness of the video and reconstruct all proposal bounding boxes jointly using the constraints that push the coefficients for each bounding box toward a common consensus, thus enforcing the coefficient similarity across multiple frames. We solve this optimization problem using the variant of two-metric projection algorithm. Finally, the video proposal that has the lowest reconstruction cost and is motion salient is used to localize the action. Our method is not only applicable to the trimmed videos, but it can also be used for action localization in untrimmed videos, which is a very challenging problem. Finally, in the third part of this dissertation, we propose a novel approach to generate a few properly ranked action proposals from a large number of noisy proposals. The proposed approach begins with dividing each proposal into sub-proposals. We assume that the quality of proposal remains the same within each sub-proposal. We, then employ a graph optimization method to recombine the sub-proposals in all action proposals in a single video in order to optimally build new action proposals and rank them by the combined node and edge scores. For an untrimmed video, we first divide the video into shots and then make the above-mentioned graph within each shot. Our method generates a few ranked proposals that can be better than all the existing underlying proposals. Our experimental results validated that the properly ranked action proposals can significantly boost action detection results. Our extensive experimental results on different challenging and realistic action datasets, comparisons with several competitive baselines and detailed analysis of each step of proposed methods validate the proposed ideas and frameworks

Masking: A New Perspective of Noisy Supervision

It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. However, such estimation is practically difficult, due to either the indirect nature of two-step approaches, or not big enough data to afford end-to-end approaches. In this paper, we propose a human-assisted approach called Masking that conveys human cognition of invalid class transitions and naturally speculates the structure of the noise transition matrix. To this end, we derive a structure-aware probabilistic model incorporating a structure prior, and solve the challenges from structure extraction and structure alignment. Thanks to Masking, we only estimate unmasked noise transition probabilities and the burden of estimation is tremendously reduced. We conduct extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as well as the industrial-level Clothing1M with agnostic noise structure, and the results show that Masking can improve the robustness of classifiers significantly.Comment: NIPS 2018 camera-ready versio

Graph Masked Autoencoder for Sequential Recommendation

While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attracted much attention in recent methods to perform data augmentation through embedding contrasting for self-supervision. However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. It naturally avoids the above issue of heavy reliance on constructing high-quality embedding contrastive views. Instead, an adaptive data reconstruction paradigm is designed to be integrated with the long-range item dependency modeling, for informative augmentation in sequential recommendation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity. Our implemented model code is available at https://github.com/HKUDS/MAERec.Comment: This paper has been published as a full paper at SIGIR 202

Next Generation of Product Search and Discovery

Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users. This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized

TopSig: Topology Preserving Document Signatures

Performance comparisons between File Signatures and Inverted Files for text retrieval have previously shown several significant shortcomings of file signatures relative to inverted files. The inverted file approach underpins most state-of-the-art search engine algorithms, such as Language and Probabilistic models. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures. Many advances in semantic hashing and dimensionality reduction have been made in recent times, but these were not so far linked to general purpose, signature file based, search engines. This paper introduces a different signature file approach that builds upon and extends these recent advances. We are able to demonstrate significant improvements in the performance of signature file based indexing and retrieval, performance that is comparable to that of state of the art inverted file based systems, including Language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and from the theoretical perspective it positions the file signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201