Study on the Development of Collaborative Filtering Systems and Its Application

Kim, Jinmook

doi:10.16881/jss.2018.04.29.2.197

Cited by 1 publication

(1 citation statement)

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“…The research minimized the system load by introducing a recommending system that restricts information based on the usefulness to the user. Nevertheless, while system load minimization is achieved, a decrease in user expectation entails 7 . While the knowledge share system and knowledge recommending system have the advantage of providing useful information to users, they have their limitations in the accommodation of kinetic information requests from kinetic users 8 .…”

Section: Related Workmentioning

confidence: 99%

Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy‐analytic hierarchy process

Park

Mesicek

Shin

et al. 2020

Concurrency and Computation

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Summary Evaluation information generated by various users is processed using various requirements and data to make recommendations for solving the problems, and it analyzes satisfaction with the results. Despite people normally utilizes the processed information for decision making, not all information, however, brings positive outcomes to users. There are some users who perceived it negatively. In order to minimize the occurrence of such negative effects, the analysis of various user requirements is essential as well as diversifying user inputs for each requirement. Consequently, the results from individual inputs must be predicted. In the past, since the system relies on a single‐expert system, it is necessary to accept and process various limitations of recommendation and multiple requirements. Therefore, the results of the recommendation also have various problems. In order to solve this problem, this study applied an analytic hierarchy process to multiadvisor configuration. In the proposed system, one or multiple advisors are defined, and after analyzing the predefined requirements, the system accepts only the requirements that can be processed and calculates the individual recommendation results. A recommendation system was going to be studied by learning all situation.

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Section: Related Workmentioning

confidence: 99%