Integration of Deep Sparse Autoencoder and Particle Swarm Optimization to Develop a Recommender System

Recommender systems are known as intelligent systems which have many applications in enormous domains such as social networks, e-commerce services, and online shopping. Deep neural networks have shown significant improvement in the performance of recommender systems by learning the latent features o...

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Vydáno v:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics s. 2524 - 2530
Hlavní autoři: Ahmadian, Milad, Ahmadi, Mahmood, Ahmadian, Sajad, Jafar Jalali, Seyed Mohammad, Khosravi, Abbas, Nahavandi, Saeid
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 17.10.2021
Témata:
Deep learning
deep sparse autoencoder
Electronic commerce
Feature extraction
Measurement
Neural networks
Prediction algorithms
PSO
recommender systems
Social networking (online)
tag
trust
ISSN:2577-1655
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Shrnutí:Recommender systems are known as intelligent systems which have many applications in enormous domains such as social networks, e-commerce services, and online shopping. Deep neural networks have shown significant improvement in the performance of recommender systems by learning the latent features of users/items based on input data. However, it is a challenging issue to how to apply deep neural networks on different resources and how to integrate their results. In this regard, we propose a recommender system in this paper based on deep sparse autoencoder and particle swarm optimization. In particular, a deep sparse autoencoder is utilized to learn latent features based on the ratings matrix, trust relationships, and tag information. Then, particle swarm optimization is used to find the optimal weights of these latent features in calculating unknown ratings. Experiments on two datasets show the superiority of the proposed method in comparison with state of the art recommender algorithms.
ISSN:2577-1655
DOI:10.1109/SMC52423.2021.9658926