Context-Adaptive Content-Based Filtering Recommender System Based on Weighted Implicit Rating Approach

Navin, K.; Murugesan, K.

doi:10.1007/978-981-16-9447-9_23

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…There are three major approaches to recommendation systems: (1) content-based filtering [16,17], (2) collaborative filtering [18,19], and (3) hybrid approaches [20][21][22]. Literally, content-based filtering methods use items' contents, such as summary, description, and profile information.…”

Section: Introductionmentioning

confidence: 99%

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Kim,

Lim

2023

Applied Sciences

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Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.

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