Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language applications such as question answering, text summarization, and machine translation. Although early NER systems are successful in producing decent recognition accuracy, they often require much human effort in carefully designing rules or features. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

A Survey on Deep Learning for Named Entity Recognition

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

Owing to the superiority of GNN in learning on graph data and its efficacy in capturing collaborative signals and sequential patterns, utilizing GNN techniques in recommender systems has gain increasing interests in academia and industry. In this survey, we provide a comprehensive review of the most recent works on GNN-based recommender systems. We proposed a classification scheme for organizing existing works. For each category, we briefly clarify the main issues, and detail the corresponding strategies adopted by the representative models. We also discuss the advantages and limitations of the existing strategies. Furthermore, we suggest several promising directions for future researches. We hope this survey can provide readers with a general understanding of the recent progress in this field, and shed some light on future developments.

Graph Neural Networks in Recommender Systems: A Survey

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area. 

Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subject

A review on deep learning for recommender systems: challenges and remedies

In music recommender systems, automatic playlist continuation is an emerging task that aims to improve users’ listening experience by recommending music in line with their musical taste. The typical approach towards this goal is to identify playlist characteristics by inspecting the existing tracks (i.e., seeds) in target playlists. However, seeds are not always available, especially when users create new playlists. For such cold-start situations, user-generated titles can be a good starting point to understand the intended purpose of users. This paper investigates the effectiveness of titles as an auxiliary data source for playlists suffering from the cold-start problem. Employing three naive recommendation models, we conduct experiments on one million music playlists from the Spotify platform. Our analyses show that the prevalent attitude in naming playlists results in highly accurate recommendations for playlists concerning a specific theme, such as albums, artists, and soundtracks. As the title space moves away from a particular theme, recommendation accuracy drops. Furthermore, the correlation between the common preference of a title and its usability in recommendation is quite weak; a title without a common sense may be useless in recommender systems, even though many users favor that title. Consequently, our findings serve as a guideline to develop title-aware recommendation approaches that can provide coherent continuations to the cold-start playlists.

Exploring playlist titles for cold-start music recommendation: an effectiveness analysis

The cold-start problem is a grand challenge in music recommender systems aiming to provide users with a better and continuous music listening experience. When a new user creates a playlist, the recommender system remains in a cold-start state until enough information is collected to identify the user’s musical taste. In such cases, playlist metadata, such as title or description, have been successfully employed to create intent recommendation models. In this paper, we propose a multi-stage retrieval system utilizing user-generated titles to alleviate the cold-start problem in automatic playlist continuation. Initially, playlists are clustered to form a music documents collection. Then, the system applies latent semantic indexing to the collection to discover hidden patterns between tracks and playlist titles. For similarity calculation, singular value decomposition is performed on a track-cluster matrix. When the system is given a new playlist as a cold-start instance, it first retrieves neighboring clusters and then produces a ranked list of recommendations by weighting candidate tracks in these clusters. We scrutinize the performance of the proposed system on a large, real-world music playlists dataset supplied by the Spotify platform. Our empirical results show that the proposed system outperforms the state-of-the-art approaches and improves recommendation accuracy significantly in three primary evaluation metrics.

Ali Yürekli

Papers

A review on deep learning for recommender systems: challenges and remedies

Exploring playlist titles for cold-start music recommendation: an effectiveness analysis

Alleviating the cold-start playlist continuation in music recommendation using latent semantic indexing