The task of keyword-based diverse image retrieval has received considerable attention due to its wide demand in real-world scenarios. Existing methods either rely on a multi-stage re-ranking strategy based on human design to diversify results, or extend sub-semantics via an implicit generator, which either relies on manual labor or lacks explainability. To learn more diverse and explainable representations, we capture sub-semantics in an explicit manner by leveraging the multi-modal knowledge graph (MMKG) that contains richer entities and relations. However, the huge domain gap between the off-the-shelf MMKG and retrieval datasets, as well as the semantic gap between images and texts, make the fusion of MMKG difficult. In this paper, we pioneer a degree-free hypergraph solution that models many-to-many relations to address the challenge of heterogeneous sources and heterogeneous modalities. Specifically, a hyperlink-based solution, Multi-Modal Knowledge Hyper Graph (MKHG) is proposed, which bridges heterogeneous data via various hyperlinks to diversify sub-semantics. Among them, a hypergraph construction module first customizes various hyperedges to link the heterogeneous MMKG and retrieval databases. A multi-modal instance bagging module then explicitly selects instances to diversify the semantics. Meanwhile, a diverse concept aggregator flexibly adapts key sub-semantics. Finally, several losses are adopted to optimize the semantic space. Extensive experiments on two real-world datasets have well verified the effectiveness and explainability of our proposed method.

News recommender systems have become an effective manner to help users make decisions by suggesting the potential news that users may click and read, which has shown the proliferation nowadays. Many representative algorithms made great efforts to discover users’ preferences from the histories for triggering news recommendations. However, there exist some limitations due to the following two main issues. First, they mainly rely on the sufficient user data, which cannot well capture users’ temporal interests with very limited records. Second, always perceiving users’ histories for recommendation may ignore some important news (e.g., breaking news). In this article, we propose a novel Multi-factors Fusion model for news recommendation by integrating both user-dependent preference effect and user-independent timeliness effect together. First, to track the preference of a certain user, we decompose her reading history into two user-related factors, including the long-term habit and the short-term interest. Specifically, we extract her persistent habit by exploring the category effect of news that she focuses on from her whole records. Then, we characterize her temporary interests by proposing a recurrent neural network of analyzing the homogeneous relations between her latest clicked news and the candidate ones. Second, to describe the user-independent news timeliness effect, we propose a novel survival analysis model to estimate the instantaneous click probability of a certain news as the occurring probability of an event, where much sensational news tends to be picked out. Last, we fuse all effects to determine the probability of a user clicking on a certain news under the independent event assumption. We conduct extensive experiments on two real-world datasets. Experimental results demonstrate that our model can generate better news recommendations on both general scenario and cold-start scenario.

Semantic segmentation labels each pixel in high-resolution remote sensing (HRRS) images with a category. To tackle with the large size and complexity of HRRS images, this letter presents a novel multiscale feature aggregation lightweight network (MFALNet) for semantic segmentation. Unlike standard convolution, asymmetric depth-wise separable convolution residual (ADCR) unit is used to reduce the parameter size of the network and makes the optimized structure deeper but lightweight and less complex. The proposed network is an encoder–decoder structure, where multiscale feature aggregation is implemented in both the encoder and the decoder. The spatial self-attention block helps to capture long-range contextual information, and the gated convolution modules are further used for refining features when aggerating high- and low-level feature maps in the decoder. The proposed MFALNet has evaluated on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2-D semantic labeling contest open benchmark data set, and the experimental results prove that the scheme can obtain a better tradeoff between segmentation accuracy and computational efficiency compared with the state-of-the-art semantic segmentation models.

Extracting change regions from bitemporal images is crucial to urban planning, land, and resources survey. In the literature, many methods obtaining difference between bitemporal remote sensing images have been proposed. However, there are still some problems due to the complexity of change conditions. In order to solve the above-mentioned problems, we propose a novel network called PPCNET, combining patch-level and pixel-level change detection for bitemporal remote sensing images. This network is divided into three branches: the dual structure is used to extract features of bitemporal images, respectively; changed or unchanged image regions are then detected through fully connected layers, and a soft-max layer at patch level. Once a change is detected at patch level, feature encoder and decoder at pixel level are activated to obtain accurate change boundary. Furthermore, a feature pyramid network-based architecture is employed to fuse information in different layers to further improve change detection effectiveness. Experiments on both satellite and aerial remote sensing images have verified that PPCNET network yields higher change detection accuracy with faster detection speed.

The problem of mobile context recognition targets the identification of semantic meaning of context in a mobile environment. This plays an important role in understanding mobile user behaviors and thus provides the opportunity for the development of better intelligent context-aware services. A key step of context recognition is to model the personalized contextual information of mobile users. Although many studies have been devoted to mobile context modeling, limited efforts have been made on the exploitation of the sequential and dependency characteristics of mobile contextual information. Also, the latent semantics behind mobile context are often ambiguous and poorly understood. Indeed, a promising direction is to incorporate some domain knowledge of common contexts, such as “waiting for a bus” or “having dinner,” by modeling both labeled and unlabeled context data from mobile users because there are often few labeled contexts available in practice. To this end, in this article, we propose a sequence-based semisupervised approach to modeling personalized context for mobile users. Specifically, we first exploit the Bayesian Hidden Markov Model (B-HMM) for modeling context in the form of probabilistic distributions and transitions of raw context data. Also, we propose a sequential model by extending B-HMM with the prior knowledge of contextual features to model context more accurately. Then, to efficiently learn the parameters and initial values of the proposed models, we develop a novel approach for parameter estimation by integrating the Dirichlet Process Mixture (DPM) model and the Mixture Unigram (MU) model. Furthermore, by incorporating both user-labeled and unlabeled data, we propose a semisupervised learning-based algorithm to identify and model the latent semantics of context. Finally, experimental results on real-world data clearly validate both the efficiency and effectiveness of the proposed approaches for recognizing personalized context of mobile users.

Collaborative Filtering(CF) is a popular way to build recommender systems and has been successfully employed in many applications. Generally, two kinds of approaches to CF, the local neighborhood methods and the global matrix factorization models, have been widely studied. Though some previous researches target on combining the complementary advantages of both approaches, the performance is still limited due to the extreme sparsity of the rating data. Therefore, it is necessary to consider more information for better reflecting user preference and item content. To that end, in this paper, by leveraging the extra tagging data, we propose a novel unified two-stage recommendation framework, named Neighborhood-aware Probabilistic Matrix Factorization(NHPMF). Specifically, we first use the tagging data to select neighbors of each user and each item, then add unique Gaussian distributions on each user's(item's) latent feature vector in the matrix factorization to ensure similar users(items) will have similar latent features}. Since the proposed method can effectively explores the external data source(i.e., tagging data) in a unified probabilistic model, it leads to more accurate recommendations. Extensive experimental results on two real world datasets demonstrate that our NHPMF model outperforms the state-of-the-art methods.

Mining the frequently visited places of single mobile users, i.e., significant places, is crucial for supporting personalized location-based services. Most of existing works for significance place mining have a need to take advantage the GPS trajectories of users. However, it is difficult to encourage mobile users to contribute GPS trajectories because of the high power consumption of GPS. In this paper, we propose a geo-grid based approach for mining significant places from cell ID trajectories. In our approach, the mined significant places are represented as sets of geo-grids which are much smaller than the coverage areas of cell-sites. To be specific, we firstly extract the stay areas where the mobile user used to stay and map them to many geo-grids. Then we mine significant places from the geo-grids by considering their significance. We evaluate the approach on real word data sets and the experimental results clearly show that the proposed approach outperforms two baselines.

Mining the frequently visited places of single mobile users, i.e., significant places, is crucial for supporting personalized location-based services. Most of existing works for significance place mining have a need to take advantage the GPS trajectories of users. However, it is difficult to encourage mobile users to contribute GPS trajectories because of the high power consumption of GPS. In this demonstration, we propose a geo-grid based approach for mining significant places from cell ID trajectories. In our approach, the mined significant places are represented as sets of geo-grids which are much smaller than the coverage areas of cell-sites. To be specific, we firstly extract the stay areas where the mobile user used to stay and map them to many geogrids. Then we mine significant places from the geo-grids by considering their significance.

In this paper, we present a systematic perspective study on choosing and evaluating the initial seed items that will be recommended to the cold start users. We first construct an item consumption correlation network to capture the existing users' general consumption behaviors. Then, we formalize initial items recommendation as the influential seed set selection problem. Along this line, we present several methods, each of which selects seed items according to different rules. Finally, the experimental results on two real-world data sets verify that with different seed items, the users' consumption numbers will be quite different. Meanwhile, the results also provide many deep insights into these selection methods and their recommended seed items.

The user interaction with the mobile device plays an important role in user habit understanding. In this paper, we propose to mine the associations between user interactions and contexts captured by mobile devices, or behavior patterns for short, from context logs to characterize the habits of mobile users. The extensive experiments on the collected real life data clearly validate the ability of our approach for mining effective behavior patterns.

Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is critical for the success of context-aware mobile services. While there are prior work on mobile context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users.