Recommendation System Using Autoencoders

Ferreira, Diana; Silva, Sofia; Abelha, António; Machado, José

doi:10.3390/app10165510

Cited by 58 publications

(26 citation statements)

References 31 publications

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“…Autoencoder techniques have been applied in various domains. For example, in civil engineering for bearing defect detections [74], health-related human activity recognition [75,76], medical imaging [77,78], recommendation systems [79][80][81], and many other domains. Figure 5 shows the components of an autoencoder algorithm.…”

Section: Autoencoders (Ae)mentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

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The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

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Section: Autoencoders (Ae)mentioning

confidence: 99%

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

2021

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“…Other approaches consider recommending products that are in line with the user’s interests without being affected by the problems faced by the recommendation system mentioned above and the problem of data sparsity. For instance in [ 34 ], a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. The experiment result shows a very low Root Mean Squared Error (RMSE) value, considering that the users’ recommendations are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse.…”

Section: Recommender Systemsmentioning

confidence: 99%

Multi-Criteria Recommendation Systems to Foster Online Grocery

Hafez

Redondo

Vilas

et al. 2021

Sensors

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With the exponential increase in information, it has become imperative to design mechanisms that allow users to access what matters to them as quickly as possible. The recommendation system (RS) with information technology development is the solution, it is an intelligent system. Various types of data can be collected on items of interest to users and presented as recommendations. RS also play a very important role in e-commerce. The purpose of recommending a product is to designate the most appropriate designation for a specific product. The major challenge when recommending products is insufficient information about the products and the categories to which they belong. In this paper, we transform the product data using two methods of document representation: bag-of-words (BOW) and the neural network-based document combination known as vector-based (Doc2Vec). We propose three-criteria recommendation systems (product, package and health) for each document representation method to foster online grocery shopping, which depends on product characteristics such as composition, packaging, nutrition table, allergen, and so forth. For our evaluation, we conducted a user and expert survey. Finally, we compared the performance of these three criteria for each document representation method, discovering that the neural network-based (Doc2Vec) performs better and completely alters the results.

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“…The idea of autoencoders exists for more than 30 years [6] and the applications are presently widespread. They range from generalization to classification tasks, denoising, anomaly detection, recommender systems, clustering and dimensionality reduction with stunning results [7,[9][10][11][12][13]. Within this work, we focus on the latter two use cases, wherein autoencoders perform unsupervised feature extraction and dimensionality reduction [14,15].…”

Section: Why Are Autoencoders Interesting?mentioning

confidence: 99%

Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics

Walch

Schichtel

Lehmann

et al. 2021

The 7th International Conference on Time Series and Forecasting

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Picking an appropriate parameter setting (meta-parameters) for visualization and embedding techniques is a tedious task. However, especially when studying the latent representation generated by an autoencoder for unsupervised data analysis, it is also an indispensable one. Here we present a procedure using a cross-correlative take on the meta-parameters. This ansatz allows us to deduce meaningful meta-parameter limits using OPTICS, DBSCAN, UMAP, t-SNE, and k-MEANS. We can perform first steps of a meaningful visual analysis in the unsupervised case using a vanilla autoencoder on the MNIST and DeepVALVE data sets.

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Recommendation System Using Autoencoders

Cited by 58 publications

References 31 publications

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Multi-Criteria Recommendation Systems to Foster Online Grocery

Meta-Parameter Selection for Embedding Generation of Latency Spaces in Auto Encoder Analytics

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