Performance of Two Approaches of Embedded Recommender Systems

Pajuelo-Holguera, Francisco; Gómez–Pulido, Juan A.; Ortega, Fernando

doi:10.3390/electronics9040546

Cited by 3 publications

(3 citation statements)

References 41 publications

(42 reference statements)

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“…The CF algorithm is discussed to confront the sparsity problem in the resulting graph partitions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency [67]. The parallel hardware implementation based algorithm is developed for embedded CF applications with large datasets [68]. The online recommendation algorithm is designed, which combines clustering and CF techniques to improve the accuracy of online recommendation systems for group-buying applications [69].…”

Section: Features-based Recommendersmentioning

confidence: 99%

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Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

2020

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Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| < 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes <10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.

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Section: Features-based Recommendersmentioning

confidence: 99%

“…Nowadays, the new recommender algorithms are required for real-world applications, because of the following reasons [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][21][22][23][24][29][30][31][32][43][44][45][46][47][66][67][68][69][70]:…”

Section: Features-based Recommendersmentioning

confidence: 99%

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

2020

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“…The information used for recommending an item is usually stored in large databases which dynamically grow and build the source of knowledge regarding user behavior, so that recommendations of items can be suggested [4]. The decision of a user in digital platforms is likely to follow the cost-benefit theory on information presentation.…”

Section: Introductionmentioning

confidence: 99%

How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Ban

Lee

2021

Electronics

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Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

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Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

2021

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Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of <1000, better metric values were produced. When the learner size exceeded 1000, significant differences were obtained in the evaluated metrics. The significant differences were analyzed in terms of a computational structure depending on L, the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods.

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Performance of Two Approaches of Embedded Recommender Systems

Cited by 3 publications

References 41 publications

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

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