A Study of Outcomes Utilizing K-MOOC Credit Based on the Information System Success Model

Joo, Young Ju; Dongsim, Kim

doi:10.15854/jes.2017.06.48.2.95

Cited by 2 publications

(1 citation statement)

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“…This paper uses the average absolute error widely used in the recommended quality test method MAE [19] and P(U)@N [20] are used as metrics. The MAE measures the accuracy of the recommendation results by calculating the deviation between the predicted rating and the actual rating [21], while P(U)@N represents the Top-N recommended column generated by the user.…”

Section: Experimental Data Sets and Evaluation Criteriamentioning

confidence: 99%

Study on the Recommended Model Based on Personal Information System

Dai¹

2018

JCC

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This paper classifies the scenario elements which affect the real-time information needs of mobile commerce users, and proposes a nomination model that integrates the user's personalized context elements. In this model, the top K scenarios that have the greatest impact on each user's instant information demands are calculated from the user's current scenario and historical data, thereby constructing a user personalized situation and improving it as an input condition that existing scenario-based multi-dimensional information recommendation algorithm is used for project nomination. Result/Conclusion: The improved algorithm and other three algorithms were compared by Movie lens and MBook Crossing dataset. The experimental results show that the model has higher prediction accuracy and can effectively improve user satisfaction and more effectively and solve personalized nomination issues in a mobile commerce environment.

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Section: Experimental Data Sets and Evaluation Criteriamentioning

confidence: 99%