Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers

Semi-Supervised Sparse Coding

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets

Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods

Visual Concept Detection in Images and Videos

The rapidly increasing proliferation of digital images and videos leads to a situation where content-based search in multimedia databases becomes more and more important. A prerequisite for effective image and video search is to analyze and index media content automatically. Current approaches in the field of image and video retrieval focus on semantic concepts serving as an intermediate description to bridge the “semantic gap” between the data representation and the human interpretation. Due to the large complexity and variability in the appearance of visual concepts, the detection of arbitrary concepts represents a very challenging task. In this thesis, the following aspects of visual concept detection systems are addressed: First, enhanced local descriptors for mid-level feature coding are presented. Based on the observation that scale-invariant feature transform (SIFT) descriptors with different spatial extents yield large performance differences, a novel concept detection system is proposed that combines feature representations for different spatial extents using multiple kernel learning (MKL). A multi-modal video concept detection system is presented that relies on Bag-of-Words representations for visual and in particular for audio features. Furthermore, a method for the SIFT-based integration of color information, called color moment SIFT, is introduced. Comparative experimental results demonstrate the superior performance of the proposed systems on the Mediamill and on the VOC Challenge. Second, an approach is presented that systematically utilizes results of object detectors. Novel object-based features are generated based on object detection results using different pooling strategies. For videos, detection results are assembled to object sequences and a shot-based confidence score as well as further features, such as position, frame coverage or movement, are computed for each object class. These features are used as additional input for the support vector machine (SVM)-based concept classifiers. Thus, other related concepts can also profit from object-based features. Extensive experiments on the Mediamill, VOC and TRECVid Challenge show significant improvements in terms of retrieval performance not only for the object classes, but also in particular for a large number of indirectly related concepts. Moreover, it has been demonstrated that a few object-based features are beneficial for a large number of concept classes. On the VOC Challenge, the additional use of object-based features led to a superior performance for the image classification task of 63.8% mean average precision (AP). Furthermore, the generalization capabilities of concept models are investigated. It is shown that different source and target domains lead to a severe loss in concept detection performance. In these cross-domain settings, object-based features achieve a significant performance improvement. Since it is inefficient to run a large number of single-class object detectors, it is additionally demonstrated how a concurrent multi-class object detection system can be constructed to speed up the detection of many object classes in images. Third, a novel, purely web-supervised learning approach for modeling heterogeneous concept classes in images is proposed. Tags and annotations of multimedia data in the WWW are rich sources of information that can be employed for learning visual concepts. The presented approach is aimed at continuous long-term learning of appearance models and improving these models periodically. For this purpose, several components have been developed: a crawling component, a multi-modal clustering component for spam detection and subclass identification, a novel learning component, called “random savanna”, a validation component, an updating component, and a scalability manager. Only a single word describing the visual concept is required to initiate the learning process. Experimental results demonstrate the capabilities of the individual components. Finally, a generic concept detection system is applied to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question addressed in the field of behavioral sciences is, whether and how playing violent content in computer games may induce aggression. Therefore, novel semantic concepts most notably “violence” are detected in computer game videos to gain insights into the interrelationship of violent game events and the brain activity of a player. Experimental results demonstrate the excellent performance of the proposed automatic concept detection approach for such interdisciplinary research