A deep learning-based hybrid model for recommendation generation and ranking

Neural Computing and Applications - A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such...     

基于深度学习的混合推荐算法

针对传统的推荐系统存在推荐精度较低且冷启动较严重的问题,综合考虑评论文本与评分而提出改进的稀疏边缘降噪自动编码与近邻项目影响力的矩阵分解模型相结合的混合推荐方法。通过改进的稀疏边缘降噪自动编码模型从商品评论文本中来提取商品特征,将用户评论与评分联合,同时综合考虑了近邻用户对于目标用户的影响,将近邻项目影响力整合到矩阵分解算法之中,与传统的协同深度学习模型(CDL)和混合SDAE模型相比,最高可提升8. 370%。     

Development and validation of a deep learning-based algorithm for drowsiness detection in facial photographs

Multimedia Tools and Applications - Drowsiness is a feeling of sleepiness before the sleep onset and has severe implications from a safety perspective for the individuals involved in industrial...     

DFuzzy: a deep learning-based fuzzy clustering model for large graphs

Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.     

Detecting earthquakes: a novel deep learning-based approach for effective disaster response

Applied Intelligence - In the present study, we present an intelligent earthquake signal detector that provides added assistance to automate traditional disaster responses. To effectively respond...     

ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting

Neural Computing and Applications - Digital forensics has a vital effect in several domains and mainly focuses on reactive measures, especially when facing digital incidents. Gender identification...     

Deep MAnTra: deep learning-based multi-animal tracking for Japanese macaques

Artificial Life and Robotics - Multi-instance object tracking is an active research problem in computer vision, where most novel methods analyze and locate targets on videos taken from static...     

Cluster center initialization algorithm for K-modes clustering

Partitional clustering of categorical data is normally performed by using K-modes clustering algorithm, which works well for large datasets. Even though the design and implementation of K-modes algorithm is simple and efficient, it has the pitfall of randomly choosing the initial cluster centers for invoking every new execution that may lead to non-repeatable clustering results. This paper addresses the randomized center initialization problem of K-modes algorithm by proposing a cluster center initialization algorithm. The proposed algorithm performs multiple clustering of the data based on attribute values in different attributes and yields deterministic modes that are to be used as initial cluster centers. In the paper, we propose a new method for selecting the most relevant attributes, namely Prominent attributes, compare it with another existing method to find Significant attributes for unsupervised learning, and perform multiple clustering of data to find initial cluster centers. The proposed algorithm ensures fixed initial cluster centers and thus repeatable clustering results. The worst-case time complexity of the proposed algorithm is log-linear to the number of data objects. We evaluate the proposed algorithm on several categorical datasets and compared it against random initialization and two other initialization methods, and show that the proposed method performs better in terms of accuracy and time complexity. The initial cluster centers computed by the proposed approach are close to the actual cluster centers of the different data we tested, which leads to faster convergence of K-modes clustering algorithm in conjunction to better clustering results.     

融合深度神经网络和张量分解的地点推荐算法

为提高利用张量分解技术进行基于位置社交网络的地点推荐的推荐性能,提出一种利用张量分解技术且融合神经网络的地点推荐算法。融合多层感知机和长短期记忆网络基于张量分解技术建模用户的签到行为,将学习到的用户偏好表示馈送到推荐生成器和推荐判别器组成的对抗生成网络中,通过对抗训练学习最佳用户偏好表示用于推荐。基于真实数据集的实验验证了该算法的有效性和高效性。     

HOPE: a hybrid deep neural model for out-of-town next POI recommendation

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

     

Multi-type factors representation learning for deep learning-based knowledge tracing

Knowledge Tracing (KT) refers to the problem of predicting future learner performance given their historical interactions with e-learning platforms. Recent years, Deep Learning-based Knowledge Tracing (DLKT) methods show superior performance than traditional methods due to their strong representational ability. However, researchers usually focus on innovations in model structure, while ignoring the importance of Representation Learning (RL) for DLKT. Investigating previous studies, it is found that the mining and integration of learning-related factors can effectively improve the performance of DLKT models. This paper focuses on providing a model embedding interface for DLKT by considering multiple types of learning-related factors. We first explore and analyze four types of learning-related factors: exercise and skill, the attributes of exercise, learners’ historical performance, and learners’ forgetting behavior in the learning process. We then propose an Extensible Representation Learning (ERL) approach for DLKT to extract and integrate the representations of these four types of factors by setting five components: base embedding, auxiliary embedding, performance embedding, forgetting embedding, and embedding integration. Finally, we apply ERL into two mainstream DLKT models and comprehensively evaluate the proposed approach on several real-world benchmark datasets. Results show that ERL can significantly improve the performances of these two network on predicting future learner responses.

     

Learning to hash: a comprehensive survey of deep learning-based hashing methods

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor search. Deep learning-based hashing methods have proved their efficacy to learn advanced hash functions that suit the desired goal of nearest neighbor search in large image-based data-sets. In this work, we present a comprehensive review of different deep learning-based supervised hashing methods particularly for image data-sets suggested by various researchers till date to generate advanced hash functions. We categorize prior works into a five-tier taxonomy based on: (i) the design of network architecture, (ii) training strategy based on nature of data-set, (iii) the type of loss function, (iv) the similarity measure and, (v) the nature of quantization. Further, different data-sets used in prior works are reported and compared based on various challenges in the characteristics of images that are part of the data-sets. Lastly, different future directions such as incremental hashing, cross-modality hashing and guidelines to improve design of hash functions are discussed. Based on our comparative review, it has been observed that generative adversarial networks-based hashing models outperform other methods. This is due to the fact that they leverage more data in the form of both real world and synthetically generated data. Furthermore, it has been perceived that triplet-loss-based loss functions learn better discriminative representations by pushing similar patterns together and dis-similar patterns away from each other. This study and its observations shall be useful for the researchers and practitioners working in this emerging research field.

     

End-to-end deep learning-based autonomous driving control for high-speed environment

The Journal of Supercomputing - With the recent emergence of artificial intelligence (AI) technology, autonomous vehicle industry has rapidly adopted this technology to investigate self-driving...     

Attribute-aware deep attentive recommendation

The Journal of Supercomputing - Since the rich semantics of attribute information has become a great supplement to the ratings data in designing recommender systems, fusing attributes information...     

TimeFly algorithm: a novel behavior-inspired movie recommendation paradigm

Pattern Analysis and Applications - This paper proposes a novel behavior-inspired recommendation algorithm named TimeFly algorithm, which works on the idea of altering behavior of the user with...     

LWSINet: A deep learning-based approach towards video script identification

Multimedia Tools and Applications - Videos – a high volume of texts – broadcast via different media, such as television and the internet. Since Optical Character Recognition (OCR)...     

A deep learning-based CEP rule extraction framework for IoT data

The Journal of Supercomputing - With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously...     

Adaptive recommendation for photo pose via deep learning

Multimedia Tools and Applications - With the development of image acquisition devices and the popularity of smart phones, more and more people would like to upload their photos to diverse social...