Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

Duong, Tan Nghia; Vuong, Tuan Anh; Nguyen, Duc Minh; Dang, Quang Hieu

doi:10.1109/access.2020.2989408

Cited by 14 publications

(21 citation statements)

References 37 publications

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“…In [39], we introduced a natural language processing (NLP)-based cleaning process to eliminate the redundancy from the original 1128 genome tags to generate a more accurate description for each movie with 1044 new tags. While this process slightly improved the accuracy of the systems, the number of new tags is still quite large (7% smaller to original 1128 tags) and other groups of related tags which cannot be combined using NLP become hidden.…”

Section: Previous Workmentioning

confidence: 99%

“…Experimental results in our previous work [39] demonstrated the capability of an AE to produce a more compact and powerful representation for movies than the original genome scores. However, the fully-connected architecture of a traditional AE does not consider the order of its raw inputs, which takes a risk that the network just tries to learn the data without extracting any more useful information [19].…”

Section: Learning New Representation Of Structured Data With An Hcaementioning

confidence: 99%

“…Experiments with different values of N provide the same results: a combination of (Identity, ReLU) provides the lowest error rate and is chosen for the Half Convolutional encoder thereafter. To thoroughly evaluate the capability of the HCAE against other AE architectures, both versions of genome tags, 1128 original and 1044 combined ones using NLP [39], are chosen as their inputs. Specifically, a vanilla AE and its two popular variants are implemented as follows:…”

Section: Utilizing Hcae In Recommendation Systemsmentioning

confidence: 99%

“…It is worth noting that the operating time of AE/DAE/VAE here does not take into account the step of NLP preprocessing which is totally eliminated when utilizing the proposed HCAE. In practice, the preprocessing stage introduced in [39] may cost a great amount of time if the number of tags increases sharply, which is highly likely to happen because they are freely generated by users. Therefore, the trade-off is totally acceptable.…”

Section: Utilizing Hcae In Recommendation Systemsmentioning

confidence: 99%

“…• kNN-Content [38]: PCC genome is used as the similarity measure. • kNN-Content AE -SVD and kNN-Content AE -SVD++ [39]: 600-feature vectors for movies are learned from 1044 NLP-preprocessed genome tags using a 3-layer AE. • FM genome [14]: each feature vector is composed of one-hot encoded user and movie ID, movie genres and genome scores associated with each movie; the model is trained with degree d = 2 and 50 iterations.…”

mentioning

confidence: 99%

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Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

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Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

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Section: Previous Workmentioning

confidence: 99%

Section: Learning New Representation Of Structured Data With An Hcaementioning

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Section: Utilizing Hcae In Recommendation Systemsmentioning

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Section: Utilizing Hcae In Recommendation Systemsmentioning

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Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

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Deep Learning-Based Recommendation System: Systematic Review and Classification

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IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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Utilizing an Autoencoder-Generated Item Representation in Hybrid Recommendation System

Cited by 14 publications

References 37 publications

Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Deep Learning-Based Recommendation System: Systematic Review and Classification

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