The Concept of Information Modeling in Interactive Intelligence Systems

Sinitsyn, Sergey; Панченко, Владимир; Kharchenko, V. K.; Kovalev, Andrey A.; Vasant, Pandian

doi:10.1007/978-3-030-33585-4_25

Cited by 1 publication

(1 citation statement)

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“…The integration might use results produced by these different systems and correlate these results to show recommendations to the online users. Thus, the future research direction will be focusing on hybrid models of recommendation systems [25]…”

Section: Discussionmentioning

confidence: 99%

Recommendation System for E-commerce Using Alternating Least Squares (ALS) on Apache Spark

Gosh

Nahar

Wahab

et al. 2021

Advances in Intelligent Systems and Computing

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Recommendation system can predict the ratings of users to items by leveraging machine learning algorithms. The use of recommendation systems is common in e-commerce websites now-a-days. Since enormous amounts of data including users' click streams, purchase history, demographics, social networking comments and user-item ratings are stored in e-commerce systems databases, the volume of the data is getting bigger at high speed, and the data is sparse. However, the recommendations and predictions must be made in real time, enabling to bring enormous benefits to human beings. Apache spark is well suited for applications which require high speed query of data, transformation and analytics results. Therefore, the recommendation system developed in this research is implemented on Apache Spark. Also, the matrix factorization using Alternating Least Squares (ALS) algorithm which is a type of collaborative filtering is used to solve overfitting issues in sparse data and increases prediction accuracy. The overfitting problem arises in the data as the user-item rating matrix is sparse. In this research a recommendation system for e-commerce using alternating least squares (ALS) matrix factorization method on Apache Spark MLlib is developed. The research shows that the RMSE value is significantly reduced using ALS matrix factorization method and the RMSE is 0.870. Consequently, it is shown that the ALS algorithm is suitable for training explicit feedback data set where users provide ratings for items. S. Gosh et al.

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Section: Discussionmentioning

confidence: 99%