Scalable deep learning-based recommendation systems

Lee, Hyeungill; Lee, Jungwoo

doi:10.1016/j.icte.2018.05.003

Cited by 42 publications

(16 citation statements)

References 6 publications

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“…Hyeungill Lee et al proposed collaborative filtering based on a deep neural networks recommendation system with user-rating and item-rating vectors as inputs. The authors claimed that their proposed approach was more scalable than and performed superiorly to the traditional collaborative filtering method [36]. Aminu Da'u et al proposed developing recommendation systems utilizing the multichannel deep convolutional neural network that serve in aspect-based opinion mining, which considers fine-grained users' opinions to enhance the accuracy of recommendation systems [37].…”

Section: Related Workmentioning

confidence: 99%

Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

Shafqat

Byun

2021

Applied Sciences

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With the ever-growing amount of online data and information, recommender systems are becoming overwhelmingly popular as an adequate approach for overcoming the challenge of information overload. Artificial Intelligence (AI) and Deep Learning (DL) have accumulated significant interest in many research areas, and recommender systems are one of them. In this paper, a Graph Convolutional Neural Network (GCNN)-based approach was used for online product recommendation. Graph-based methods have undergone substantial consideration for several recommendation tasks, with effective results. However, handling the computational complexities and training large datasets remain a challenge for such a model. Even though they are useful, the excessive measure of the model’s boundaries obstructs their applications in real-world recommender frameworks to a great extent. The recursive way of generating neighbor node embeddings for each node in the graph makes it more challenging to train a deep and large GCNN model. Therefore, we propose a model that incorporates measures of similarity between two different nodes, and these similarity measures help us to sample the neighbors beforehand. We estimate the similarity based on their interaction probability distribution with other nodes. We use KL divergence on different probability distributions to find the distance between them. This way, we set a threshold criterion for neighbor selection and generate other clusters. These clusters are then converted to subgraphs and are used as input for the proposed GCNN model. This approach simplifies the task of neighbor sampling for GCNN, and hence, we can observe a significant improvement in the computational complexity of the GCNN model. Finally, we compared the results with those for the previously proposed OpGCN model, basic GCNN model, and other traditional approaches such as collaborative filtering and probabilistic matrix factorization. The experiments showed that the complexity and computational time were decreased by estimating the similarity among nodes and sampling the nodes before training.

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Section: Related Workmentioning

confidence: 99%

Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

Shafqat

Byun

2021

Applied Sciences

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“…Deep learning is a mechanism that use a structure of human brain as an artificial neural network so that computer application can has similar cognitive action like the normal human being. The purpose of deep learning is to increase the relevance of recommendations and to provide the results in a scalable way [20]. As [21] mentioned, deep learning was a crucial part in the recommendation system.…”

Section: Deep Learningmentioning

confidence: 99%

Collaborative filtering content for parental control in mobile application chatting

Razali¹,

Ahmad²,

Diah³

2019

Bulletin EEI

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Mobile phone is an important medium of communication for most people regardless their age. One of the most used mobile phone application is chat application. Since mobile users are across different age groups, from youngsters to senior citizen, each age level has their own ways or styles of communication when using mobile application chatting. Adult mostly used proper syntax with complete sentences while youngsters normally use short forms with incomplete sentences. In addition, improper communication styles, usage of bad and inappropriate words has become a trend among youngsters. This matter gives negative impact on the education system in a short-term and national language preservation in a long term. Hence, in this paper, researchers present a tool that provides word recommendations for mobile application chatting. This tool would be is an education material to younger generation users with subtle approach. Implementation of the tool is by adapting Collaborative Filtering approach with User-Based model which focusing on recommendation on similar interest between users. Collaborative filtering content tool is functioning well during the functionality testing and it is thriving as a mobile application chatting guidance.

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“…Moreover, scalability is the main concern for researchers using deep learning for social recommendation. In [4], It is clearly mentioned that neural network can not work properly due to very few entries in useritem matrix. It is necessary to normalize the values so that neural networks are trained effectively.…”

Section: Deep Learning Based Recommendationmentioning

confidence: 99%

“…Ratings based recommendations are provided in collaborative filtering technique. Memory-based and model-based are different methods in collaborative filtering [4]. In memory-based CF, ratings of users are considered for recommendation.…”

Section: Introductionmentioning

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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Scalable deep learning-based recommendation systems

Cited by 42 publications

References 6 publications

Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

Collaborative filtering content for parental control in mobile application chatting

Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning

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