User behavior analysis and commodity recommendation for point-earning apps

Chen, Yu-Ching; Yang, Chia-Ching; Liau, Yan-Jian; Chang, Chia‐Hui; Chen, Pin-Liang; Yang, Ping-Che; Ku, Tsun

doi:10.1109/taai.2016.7880109

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…Chen et al [7]used the CCAM (co-clustering with augmented matrices) to develop multiple methods like heuristic scoring, conventional classification and machine learning to construct a recommendation system as well as the integration of content-based hybrid recommendation systems in collaboration with collaborative filtering model. Zhou et al [8] developed a collaborative filtering based ALS Algorithm for the Netflix Prize.ALS works to solve scalability issue of extensive datasets.…”

Section: Literature Reviewmentioning

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: Literature Reviewmentioning

confidence: 99%