A Deep Autoencoder-Based Hybrid Recommender System

Bougteb, Yahya; Ouhbi, Brahim; Frikh, Bouchra; Zemmouri, El Moukhtar

doi:10.4018/ijmcmc.297963

Cited by 6 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A deep autoencoder is powerful for reducing the data dimensions. Bougteb et al [18] helped accurately fill in the missing data values. The autoencoder learns the user's interest, reconstructs the user's missing ratings, and then uses singular value decomposition (SVD)++ decomposition to hold information on correlation; it also provides a deep analysis of the top N recommendation items.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Samadhiya,

2024

Electronics

View full text Add to dashboard Cite

Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Samadhiya,

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…al. [11], investigated a hybrid recommender system that combines a deep autoencoder for learning user interests and reconstructing missing ratings, along with SVD++ decomposition to capture correlations between different feature factors. In paper [12] Kamble studied the performance of recommender systems by leveraging data mining techniques by using an SVM-based recommender system and conducts experiments on various product datasets.…”

Section: Background and Related Workmentioning

confidence: 99%

A Hybrid Travel Recommender Model Based on Deep Level Autoencoder And Machine Learning Algorithms

V.K,

K.P

2023

jaz

View full text Add to dashboard Cite

This research investigates the application of autoencoders in processing travelogues written in the Malayalam language on Facebook. The main objective is to harness the capabilities of autoencoders to learn a compressed representation of the input data and employ it to train various machine learning models for enhanced accuracy and efficiency. The major challenge of unavailability of a benchmark dataset in the Malayalam language for the travel domain was overcome by employing NLP techniques on the unstructured, lengthy, imbalanced travelogues, applying some additional filtering methods, and the creation of an exclusive Part of Travel Tagger (POT Tagger) along with lookup dictionaries. As this pioneering work focuses on Malayalam travel reviews posted on social media, the model presents a valuable opportunity for extension to other low-resourced Indian languages. The study follows a two-step approach. Initially, an autoencoder neural network architecture is utilized to encode the travelogues into a lowerdimensional latent space representation. The encoder network adeptly captures crucial features and patterns within the data. The compressed representation obtained from the encoder is then fed into the decoder, which reconstructs the original travelogues. Subsequently, the encoded model is employed to train diverse machine learning models, including logistic regression, decision tree classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbours (KNN), stochastic gradient descent (SGD), and multilayer perceptron (MLP). By utilizing the encoded features as inputs, these models effectively learn from the concise representation of the Malayalam travelogues. Experimental results reveal that the trained machine learning models, using the encoded features, achieve higher accuracy rates compared to conventional approaches. This improvement demonstrates the effectiveness of autoencoders in capturing and representing vital characteristics of the Malayalam travelogues on Facebook. By leveraging capabilities of autoencoder model, we successfully learned a compressed representation of the input data, attaining an impressive validation accuracy of 95.84%. This finding highlights the potential of autoencoders to enhance the overall accuracy and efficiency of travel recommendation systems for Malayalam users on social media platforms.

show abstract

“…Studies have shown that the relevance of recommended items for a specific user is dependent on various criteria that the user considers when making a decision [8,18]. Furthermore, the use of multiple criteria can also help address the issue of the cold start problem, which arises when there is insufficient data to make recommendations for new users or items [19]. Therefore, incorporating multicriteria rating systems into recommendation systems can lead to more effective and personalized recommendations for users.…”

Section: Related Work 21 Multi-criteria Recommender Systemsmentioning

confidence: 99%

“…Additionally, DL has been applied in the field of text mining for topics detection and classification [37]. Furthermore, DL has been used in developing recommender systems, where it has achieved impressive performance in predicting user preferences and providing personalized recommendations [19,23,[38][39][40]. In particular, due to the capacity to automatically encode the representation of learnt features and the ability to perform more sophisticated nonlinear transformations.…”

Section: Deep Learning-based Recommender Systemsmentioning

confidence: 99%

Deep Learning-Driven Multi-Criteria Decision-Making for Effective Recommender Systems

2023

IJIES

View full text Add to dashboard Cite

Recommender systems are extensively employed across various domains, encompassing movies, hotels, and restaurants, with the aim of delivering personalized recommendations to users. However, the conventional approach of relying solely on a single rating may fall short in comprehending the underlying factors contributing to a user's overall rating. As a remedy, multi-criteria recommender systems (MCRSs) have emerged as an alternative paradigm, allowing users to rate items based on multiple dimensions. The primary challenge lies in predicting the overall rating considering a user's multi-criteria scores. In this study, we propose a MCRS that leverages a multicriteria decision-making method, which takes into account the interdependence among criteria. To this end, the weight assigned to each criterion is calculated using the correlation coefficient and standard deviation (CCSD) approach. Furthermore, we employ a deep autoencoder to capture intricate nonlinear relationships and generate recommendations of heightened accuracy. Evaluating our proposed method on the well-known MovieLens and TripAdvisor datasets, we demonstrate its superior performance compared to several single and multicriteria recommender systems. The results highlight the effectiveness of our approach in capturing pertinent aspects and achieving lower prediction errors, showcasing its potential in enhancing recommendation quality and personalization.

show abstract

A Deep Autoencoder-Based Hybrid Recommender System

Cited by 6 publications

References 39 publications

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements

A Hybrid Travel Recommender Model Based on Deep Level Autoencoder And Machine Learning Algorithms

Deep Learning-Driven Multi-Criteria Decision-Making for Effective Recommender Systems

Contact Info

Product

Resources

About