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A Prospective Extension Through an Analysis of the Existing Movie Recommendation Systems and Their Challenges

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초록

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movierecommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filteringconsiders user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches thatcombine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, severalAI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paperaims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendationsystems and their challenges.

Abstract

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movierecommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filteringconsiders user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches thatcombine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, severalAI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paperaims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendationsystems and their challenges.

참고문헌 (69)

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