Search and recommendation (S&R) are fundamental components of modern commercial platforms, enabling users to access and explore information efficiently. User behaviors in these scenarios reflect different aspects of user intent, providing an opportunity for joint modeling of S&R. However, effectively leveraging search logs to enhance recommendation remains a challenging task. Existing methods often encode S&R histories either jointly or separately; however, they tend to regard all search signals as equally informative, thereby neglecting that many search behaviors can be irrelevant or even detrimental to recommendation performance. In practice, however, search histories frequently contain noisy or outdated behaviors that may introduce spurious correlations and degrade recommendation performance. Motivated by the human decision-making process, where one first identifies recommendation intent and then selectively reasons about relevant search signals, we propose LCR-SER, a latent cross reasoning method for search-enhanced recommendation. LCR-SER first encodes the user's S&R history into a unified latent representation that captures users' global interests. It then performs iterative reasoning in the latent space to dynamically identify informative search signals that are most relevant to the recommendation. To further guide this reasoning process, we introduce contrastive learning to align the reasoning states with the target items. In addition, we employ reinforcement learning to directly optimize ranking-oriented metrics, enabling LCR-SER to refine its reasoning strategy toward improved recommendation performance. Experiments on public datasets demonstrate that LCR-SER consistently outperforms strong baselines, validating the effectiveness of latent reasoning in enhancing search-aware recommendation.

In modern online platforms, search and recommendation (S&R) often coexist, offering opportunities for performance improvement through search-enhanced approaches. Existing studies show that incorporating search signals boosts recommendation performance. However, the effectiveness of these methods relies heavily on rich search interactions. They primarily benefit a small subset of users with abundant search behavior, while offering limited improvements for the majority of users who exhibit only sparse search activity. To address the problem of sparse search data in search-enhanced recommendation, we face two key challenges : (1) how to learn useful search features for users with sparse search interactions, and (2) how to design effective training objectives under sparse conditions. Our idea is to leverage the features of users with rich search interactions to enhance those of users with sparse search interactions. Based on this idea, we propose GSERec, a method that utilizes message passing on the User-Code Graphs to alleviate data sparsity in Search-Enhanced Recommendation. Specifically, we utilize Large Language Models (LLMs) with vector quantization to generate discrete codes, which connect similar users and thereby construct the graph. Through message passing on this graph, embeddings of users with rich search data are propagated to enhance the embeddings of users with sparse interactions. To further ensure that the message passing captures meaningful information from truly similar users, we introduce a contrastive loss to better model user similarities. The enhanced user representations are then integrated into downstream search-enhanced recommendation models. Experiments on three real-world datasets show that GSERec consistently outperforms baselines, especially for users with sparse search behaviors.

Image classification has been widely adopted by current social media applications. Compared with fully supervised classification, semisupervised classification attracts more attention because it is commonly observed that category labels are only available for a small portion of images while most images on social media platforms do not have labels. To this end, we propose a two-stage semisupervised learning framework. In the first stage, we train two Self-supervised Models (SSMs). One model is initialized by predicting the rotation angles of pretransformed training images and then further trained by the labeled images. The other model is initialized by making consistent predictions for the transformed images in color, shape, and quality from the same sample image, and then further trained by the labeled images. In the second stage, we fuse the two SSMs through deep mutual learning, which enhances each of the two SSMs with the complementary information provided by the other such that the correct prediction could be shared. Experimental results on CIFAR and Caltech-256 data sets demonstrate the effect of the proposed framework.

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k -block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.

In this article, a novel ensemble model, called Multiple Kernel Ensemble Learning (MKEL), is developed by introducing a unified ensemble loss. Different from the previous multiple kernel learning (MKL) methods, which attempt to seek a linear combination of basis kernels as a unified kernel, our MKEL model aims to find multiple solutions in corresponding Reproducing Kernel Hilbert Spaces (RKHSs) simultaneously. To achieve this goal, multiple individual kernel losses are integrated into a unified ensemble loss. Therefore, each model can co-optimize to learn its optimal parameters by minimizing a unified ensemble loss in multiple RKHSs. Furthermore, we apply our proposed ensemble loss into the deep network paradigm and take the sub-network as a kernel mapping from the original input space into a feature space, named Deep-MKEL (D-MKEL). Our D-MKEL model can utilize the diversified deep individual sub-networks into a whole unified network to improve the classification performance. With this unified loss design, our D-MKEL model can make our network much wider than other traditional deep kernel networks and more parameters are learned and optimized. Experimental results on several mediate UCI classification and computer vision datasets demonstrate that our MKEL model can achieve the best classification performance among comparative MKL methods, such as Simple MKL, GMKL, Spicy MKL, and Matrix-Regularized MKL. On the contrary, experimental results on large-scale CIFAR-10 and SVHN datasets concretely show the advantages and potentialities of the proposed D-MKEL approach compared to state-of-the-art deep kernel methods.

Unsupervised domain adaptation (UDA) for person re-identification (re-ID) is a challenging task due to large variations in human classes, illuminations, camera views, and so on. Currently, existing UDA methods focus on two-domain adaptation and are generally trained on one labeled source set and adapted on the other unlabeled target set. In this article, we put forward a new issue on person re-ID, namely, unsupervised multi-target domain adaptation (UMDA). It involves one labeled source set and multiple unlabeled target sets, which is more reasonable for practical real-world applications. Enabling UMDA has to learn the consistency for multiple domains, which is significantly different from the UDA problem. To ensure distribution consistency and learn the discriminative embedding, we further propose the Camera Identity-guided Distribution Consistency method that performs an alignment operation for multiple domains. The camera identities are encoded into the image semantic information to facilitate the adaptation of features. According to our knowledge, this is the first attempt on the unsupervised multi-target domain adaptation learning. Extensive experiments are executed on Market-1501, DukeMTMC-reID, MSMT17, PersonX, and CUHK03, and our method has achieved very competitive re-ID accuracy in multi-target domains against numerous state-of-the-art methods.

The manifold of the dataset turns out to be quite useful in refining the retrieval results, and the diffusion process provides an efficient solution by careful selection of the similarity neighborhood which is usually modeled as the K-nearest neighborhood (KNN) graph. However, existing works are sensitive to the topology noises induced by the first K neighbors. In this paper, we tackle the problem by studying metric transformation which aims at finding new functional relationship to dig the latent similarity. The advantage of the approach lies in its robustness towards the varying K values; that is to say, it could preserve high similarity performances even if K is very large. Except for discussing only the global KNN (i.e. the same K for all neighborhoods) graph, we also investigate to specify a different K for each neighborhood by incorporating the new penalized consensus information (PCI). We show that PCI works superior compared with the original consensus information for denoising. Experiments on multiple affinity matrices have corroborated the superiority of our method with surprising good results.

In this paper, an automatic image-text alignment algorithm is developed for achieving more accurate indexing and retrieval of large-scale web images. First, large-scale web pages are crawled, where the informative images and their most relevant auxiliary text blocks are extracted. Second, parallel image clustering is performed to partition large-scale informative web images into a large number of clusters. By grouping the visually-similar (near-duplicate) web images into the same cluster, our parallel image clustering algorithm can significantly reduce the huge uncertainty on the relatedness between the web images and their auxiliary text terms, which can provide a good starting point for supporting automatic image-text alignment. Finally, a relevance re-ranking algorithm is developed to identify the most relevant visual text terms for the visually-similar web images in the same cluster. Our experiments on large-scale web images have obtained very positive results.

Real-world datasets often involve multiple views of data items, e.g., a Web page can be described by both its content and anchor texts of hyperlinks leading to it; photos in Flickr could be characterized by visual features, as well as user contributed tags. Different views provide information complementary to each other. Synthesizing multi-view features can lead to a comprehensive description of the data items, which could benefit many data analytic applications. Unfortunately, the simple idea of concatenating different feature vectors ignores statistical properties of each view and usually incurs the “curse of dimensionality” problem. We propose Multi-view Concept Learning (MCL), a novel nonnegative latent representation learning algorithm for capturing conceptual factors from multi-view data. MCL exploits both multi-view information and label information. The key idea is to learn a common latent space across different views which (1) captures the semantic relationships between data items through graph embedding regularization on labeled items, and (2) allows each latent factor to be associated with a subset of views via sparseness constraints. In this way, MCL could capture flexible conceptual patterns hidden in multi-view features. Experiments on a toy problem and three real-world datasets show that MCL performs well and outperforms baseline methods.

Research of graffiti character recognition and retrieval, as a branch of traditional optical character recognition (OCR), has started to gain attention in recent years. We have investigated the special challenge of the graffiti image retrieval problem and propose a series of novel techniques to overcome the challenges. The proposed bounding box framework locates the character components in the graffiti images to construct meaningful character strings and conduct image-wise and semantic-wise retrieval on the strings rather than the entire image. Using real world data provided by the law enforcement community to the Pacific Northwest National Laboratory, we show that the proposed framework outperforms the traditional image retrieval framework with better retrieval results and improved computational efficiency.

In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-level image annotation automatically. First, the semantic gap between the low-level computable visual features and users' real information needs is partitioned into four smaller gaps, and multiple approachesallare proposed to bridge these smaller gaps more effectively. To learn more reliable contextual relationships between the atomic image concepts and the co-appearances of salient objects, a multi-modal boosting algorithm is proposed. To enable hierarchical image classification and avoid inter-level error transmission, a hierarchical boosting algorithm is proposed by incorporating concept ontology and multi-task learning to achieve hierarchical image classifier training with automatic error recovery. To bridge the gap between the computable image concepts and the users' real information needs, a novel hyperbolic visualization framework is seamlessly incorporated to enable intuitive query specification and evaluation by acquainting the users with a good global view of large-scale image collections. Our experiments on large-scale image databases have also obtained very positive results.

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Multi-level annotation of images is a promising solution to enable more effective semantic image retrieval by using various keywords at different semantic levels. In this paper, we propose a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components and the relevant semantic concepts. In contrast to the well-known image-based and region-based approaches, we use the salient objects as the dominant image components to achieve automatic image annotation at the content level. By using the salient objects for image content representation, a novel image classification technique is developed to achieve automatic image annotation at the concept level. To detect the salient objects automatically, a set of detection functions are learned from the labeled image regions by using Support Vector Machine (SVM) classifiers with an automatic scheme for searching the optimal model parameters. To generate the semantic concepts, finite mixture models are used to approximate the class distributions of the relevant salient objects. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. We have also demonstrated that our algorithms are very effective to enable multi-level annotation of natural scenes in a large-scale dataset.

Semantic video classification has become an active research topic to enable more effective video retrieval and knowledge discovery from large-scale video databases. However, most existing techniques for classifier training require a large number of hand-labeled samples to learn correctly. To address this problem, we have proposed a semi-supervised framework to achieve incremental classifier training by integrating a limited number of labeled samples with a large number of unlabeled samples. Specifically, this emi-supervised framework includes: (a) Modeling the semantic video concepts by using the finite mixture models to approximate the class distributions of the relevant salient objects; (b) Developing an adaptive EM algorithm to integrate the unlabeled samples to achieve parameter estimation and model selection simultaneously; The experimental results in a certain domain of medical videos are also provided.

To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.

Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.

In our project, we are adopting a new approach for handling video data. We view the video as a well-defined data type with its own description, parameters and applicable methods. The system is based on PREDATOR, an open-source object-relational DBMS. PREDATOR uses Shore as the underlying storage manager. Supporting video operations (storing, searching-by-content and streaming) and new query types (query-by-example and multi-feature similarity searching) requires major changes in many of the traditional system components. More specifically, the storage and buffer manager has to deal with huge volumes of data with real-time constraints. Query processing has to consider the video methods and operators in generating, optimizing and executing the query plans.

This paper investigates the problem of augmenting labeled data with unlabeled data to improve classification accuracy. This is significant for many applications such as image classification where obtaining classification labels is expensive, while large unlabeled examples are easily available. We investigate an Expectation Maximization (EM) algorithm for learning from labeled and unlabeled data. The reason why unlabeled data boosts learning accuracy is because it provides the information about the joint probability distribution. A theoretical argument shows that the more unlabeled examples are combined in learning, the more accurate the result. We then introduce B-EM algorithm, based on the combination of EM with bootstrap method, to exploit the large unlabeled data while avoiding prohibitive I/O cost. Experimental results over both synthetic and real data sets that the proposed approach has a satisfactory performance.