Deep Learning-Based Recommendation: Current Issues and Challenges

Fakhfakh, Rim; Ammar, Anis Ben; Amar, Chokri Ben

doi:10.14569/ijacsa.2017.081209

Cited by 11 publications

(10 citation statements)

References 51 publications

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“…A comprehensive survey with deep insight's for developing deep learning-based approaches and techniques for recommender systems is given by Zhang et al [15]. Fakhfakh et al has presented advances in deep learning-based recommendations with emphasis on current issues and challenges [16]. In the scenario of sequence modelling and session-based recommendations Hidasi et al [17] have proposed Gated Recurrent Units based approach.…”

Section: Recurrent Neural Network-based Recommendersmentioning

confidence: 99%

A Product Recommendation Model Based on Recurrent Neural Network

Nelaturi¹,

Devi²

2019

JESA

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In traditional recommender systems, the product recommendations are generally made based on the static behavior or preference of customers. This paper designs a novel production recommendation model that processes customer-product interaction data as a time-based sequential data, and makes personalized product recommendations based on the purchase patterns of customers. Specifically, the model relies on the deep learning technique of recurrent neural network (RNN) to uncover the dynamics in purchase patterns of customers; a bidirectional model with attention mechanism was introduced to personalize the product recommendations. The effectiveness of the proposed model was verified through an experiment on a benchmark dataset called Movie lens. The experimental results show that the RNN-based model can efficiently capture the temporal dynamics of customer preferences, and then generate highly individualized product recommendations.

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Section: Recurrent Neural Network-based Recommendersmentioning

confidence: 99%

A Product Recommendation Model Based on Recurrent Neural Network

Nelaturi¹,

Devi²

2019

JESA

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“…Multilayer perceptron can be applied on user-item ratings to improve recommender systems. It is the simplest model [14] .It can approximate any function [15]. The advantage of using this model is that data need not be input separately as it is directly be used in multilayer neural network model.…”

Section: Deep Learning Based Recommendationmentioning

confidence: 99%

“…In deep belief network, many hidden layers and one observation layer exist. It is combination of Restricted Boltzmann Machines and feed-forward model [14].  Recurrent Neural Network is used in applications where hidden layers are to be processed many times.…”

Section: ( ) ( )  mentioning

confidence: 99%

Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

Bathla¹,

Rani²,

Aggarwal³

2018

ijacsa

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Recommendation is very crucial technique for social networking sites and business organizations. It provides suggestions based on users' personalized interest and provide users with movies, books and topics links that would be most suitable for them. It can improve user effectiveness and business revenue by approximately 30%, if analyzed in intelligent manner. Social recommendation systems for traditional datasets are already analyzed by researchers and practitioners in detail. Several researchers have improved recommendation accuracy and throughput by using various innovative approaches. Deep learning has been proven to provide significant improvements in image processing and object recognition. It is machine learning technique where hidden layers are used to improve outcome. In traditional recommendation techniques, sparsity and cold start are limitations which are due to less user-item interactions. This can be removed by using deep learning models which can improve user-item matrix entries by using feature learning. In this paper, various models are explained with their applications. Readers can identify best suitable model from these deep learning models for recommendation based on their needs and incorporate in their techniques. When these recommendation systems are deployed on large scale of data, accuracy degrades significantly. Social big graph is most suitable for large scale social data. Further improvements for recommendations are explained with the use of large scale graph partitioning. MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) are used as evaluation parameters which are used to prove better recommendation accuracy. Epinions, MovieLens and FilmTrust datasets are also shown as most commonly used datasets for recommendation purpose.

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“…Deep learning (DL) uses supervised learning based on digital artificial neural networks to allows a program, for example, to recognize the content of an image [Fak17] or to understand spoken language like Siri, Cortana and Google Now. With traditional methods, the machine simply compares the pixels.…”

Section: Proposed Architecturementioning

confidence: 99%

A new model driven architecture for deep learning-based multimodal lifelog retrieval

Abdallah¹,

Feki²,

Ammar³

et al. 2018

CSRN

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Nowadays, taking photos and recording our life are daily task for the majority of people. The recorded information helped to build several applications like the self-monitoring of activities, memory assistance and long-term assisted living. This trend, called lifelogging, interests a lot of research communities such as computer vision, machine learning, human-computer interaction, pervasive computing and multimedia. Great effort have been made in the acquisition and the storage of captured data but there are still challenges in managing, analyzing, indexing, retrieving, summarizing and visualizing these captured data. In this work, we present a new model driven architecture for deep learning-based multimodal lifelog retrieval, summarization and visualization. Our proposed approach is based on different models integrated in an architecture established on four phases. Based on Convolutional Neural Network, the first phase consists of data preprocessing for discarding noisy images. In a second step, we extract several features to enhance the data description. Then, we generate a semantic segmentation to limit the search area in order to better control the runtime and the complexity. The second phase consist in analyzing the query. The third phase which based on Relational Network aims at retrieving the data matching the query. The final phase treat the diversity-based summarization with k-means which offers, to lifelogger, a key-frame concept and context selection-based visualization.

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Deep Learning-Based Recommendation: Current Issues and Challenges

Cited by 11 publications

References 51 publications

A Product Recommendation Model Based on Recurrent Neural Network

A Product Recommendation Model Based on Recurrent Neural Network

Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

A new model driven architecture for deep learning-based multimodal lifelog retrieval

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