Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

Gao, Ying; Ran, Lingxi

doi:10.1109/access.2019.2935224

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…With the rapid growth of the internet, users have to spend a substantial amount of time finding appropriate books owing to the vast amount of information available. To solve this problem, several online bookstores are now equipped with book recommendation systems, which present book suggestions to satisfy user requirements (Chen et al , 2004; Gao and Ran, 2019; Yan and Xie, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

What books will be your bestseller? A machine learning approach with Amazon Kindle

Lee

Kim

et al. 2021

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Purpose With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions. Design/methodology/approach The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used. Findings The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%. Originality/value This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

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Section: Literature Reviewmentioning

confidence: 99%

What books will be your bestseller? A machine learning approach with Amazon Kindle

Lee

Kim

et al. 2021

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“…According to (3), iteratively update P and Q, the formula is shown in ( 6) and (7). ' 2…”

Section: Traditional Funksvd Algorithmmentioning

confidence: 99%

“…Therefore, collaborative filtering recommendation algorithm, as one of the information filtering technologies, is widely used in recommendation system [4,5,6]. Collaborative filtering recommendation algorithms are mainly divided into user-based collaborative filtering, itembased collaborative filtering, and model-based collaborative filtering [7]. Reference [8] developed a novel approach that incorporates a bipartite network into user-based collaborative filtering for enhancing the recommendation quality of new users.…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithm of Improved FunkSVD Based on Spark

2021

KSII TIIS

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In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

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“…The technology based on collaborative filtering mainly relies on the historical records provided by users to predict the items they are interested in and mainly depends on the scoring data, which is easy to implement and has high recommendation accuracy [7]. Collaborative filtering has become the most popular recommendation algorithm at present [8]. It uses user scores to build user-user or item-item similarity index and identifies the "nearest neighbor" of users or items to generate recommendations.…”

Section: Introductionmentioning

confidence: 99%

Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation

2022

Wireless Communications and Mobile Computing

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In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing recommendation algorithms have poor recommendation results in sparse data sets, this paper proposes a machine learning method for recommendation rating prediction based on user interest concept lattice. Firstly, the nearest neighbors are divided into direct nearest neighbors and indirect nearest neighbors by user interest concept lattice. Then, different methods are used to calculate the similarity between the direct “nearest neighbor” and the target user, and the similarity between the indirect “nearest neighbor” and the target user. Finally, the invisible item score of the target user is calculated by the similarity value. Experiments are carried out on real data sets, and the experimental results show that the CFCNN-CL algorithm and RRP-UI CL algorithm proposed in this paper have high recommendation accuracy and still have good performance in the case of sparse data.

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Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

Cited by 16 publications

References 23 publications

What books will be your bestseller? A machine learning approach with Amazon Kindle

What books will be your bestseller? A machine learning approach with Amazon Kindle

Parallel Algorithm of Improved FunkSVD Based on Spark

Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation

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