Music Individualization Recommendation System Based on Big Data Analysis

Pengfei, Sun

doi:10.1155/2022/7646000

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Big data generally consist of many basic data and valuable knowledge can be excavated by expanding these data. Occasionally useful knowledge can be found even in error data so, the researchers can mine more valuable information from the big data [1]. The advancement of big data is resulting a huge redundancy problem that interfered with the process of knowledge obtaining.…”

Section: Introductionmentioning

confidence: 99%

Big data cloud-based recommendation system using NLP techniques with machine and deep learning

Omar¹,

Frikha²,

Jumaa³

2023

TELKOMNIKA

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Recommendation systems (RS) are crucial for social networking sites. Without it, finding precise products is harder. However, existing systems lack adequate efficiency, especially with big data. This paper presents a prototype cloud-based recommendation system for processing big data. The proposed work is implemented by utilizing the matrix factorization method with three approaches. In the first approach, singular value decomposition (SVD) is used, which is an old and traditional recommendation technique. The second recommendation approach is fine-tuned using the alternating least squares (ALS) algorithm with Apache Spark. Finally, the deep neural network (DNN) algorithm is utilized with TensorFlow. This study solves the challenge of handling large-scale datasets in the collaborative filtering (CF) technique after tuning the algorithms by adjusting the parameters in the second approach, which uses machine learning, as well as in the third approach, which uses deep learning. Furthermore, the results of these two approaches outperformed conventional techniques and achieved an acceptable computational time. The dataset size is about 1.5 GB and it is collected from the Goodreads website API. Moreover, the Hadoop distributed file system (HDFS) is used as cloud storage instead of the computer's local disk for handling larger dataset sizes in the future.

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Section: Introductionmentioning

confidence: 99%

Big data cloud-based recommendation system using NLP techniques with machine and deep learning

Omar¹,

Frikha²,

Jumaa³

2023

TELKOMNIKA

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Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

Guo

Chen

et al. 2023

Mathematics

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With the rapid growth of the Internet, a wealth of movie resources are readily available on the major search engines. Still, it is unlikely that users will be able to find precisely the movies they are more interested in any time soon. Traditional recommendation algorithms, such as collaborative filtering recommendation algorithms only use the user’s rating information of the movie, without using the attribute information of the user and the movie, which has the problem of inaccurate recommendations. In order to achieve personalized accurate movie recommendations, a movie recommendation algorithm based on a multi-feature attention mechanism with deep neural networks and convolutional neural networks is proposed. In order to make the predicted movie ratings more accurate, user attribute information and movie attribute information are added, user network and movie network are presented to learn user features and movie features, respectively, and a feature attention mechanism is proposed so that different parts contribute differently to movie ratings. Text features are also extracted using convolutional neural networks, in which an attention mechanism is added to make the extracted text features more accurate, and finally, personalized movie accurate recommendations are achieved. The experimental results verify the effectiveness of the algorithm. The user attribute features and movie attribute features have a good effect on the rating, the feature attention mechanism makes the features distinguish the degree of importance to the rating, and the convolutional neural network adding the attention mechanism makes the extracted text features more effective and achieves high accuracy in MSE, MAE, MAPE, R2, and RMSE indexes.

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Music Individualization Recommendation System Based on Big Data Analysis

Cited by 2 publications

References 16 publications

Big data cloud-based recommendation system using NLP techniques with machine and deep learning

Big data cloud-based recommendation system using NLP techniques with machine and deep learning

Personalized Movie Recommendations Based on a Multi-Feature Attention Mechanism with Neural Networks

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