Implementasi metode mixed hybrid recommendation system dengan menggabungkan content-based filtering dan collaborative filtering pada sistem informasi filmku
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Mufti, Robbani
Subject
T Technology (General)
Datestamp
2021-04-26 03:09:59
Abstract :
Technological developments in the last few years developed rapidly, especially in information technology. This development brought a serious impact on the number of data and the spread of information on the internet so required the existence of a recommendation system to sort the information in accordance with the needs of the user. This research method using Mixed Hybrid by combining the two methods that is Content-based Filtering and item-based Collaborative Filtering. The merger of both of these methods are aimed at overcoming the weaknesses of each method. The algorithms used, namely Apriori on a Content-based Filtering and Cosine Similarity Centered on Collaborative Filtering. The experiment was performed on FilmKu information system by using Movielens dataset-100k. The results obtained in the form of a list of 10 recommendations of movies with a value of MAE and the ratio of the largest 0.8779 in ruleset reached 42%. Keywords: Apriori, Content-based Filtering, Centered Cosine Similarity, Itembased Collaborative Filtering, Lift Ratio, MAE, Mixed Hybrid, Movielens, Recommendation System.
Technological developments in the last few years developed rapidly, especially in information technology. This development brought a serious impact on the number of data and the spread of information on the internet so required the existence of a recommendation system to sort the information in accordance with the needs of the user. This research method using Mixed Hybrid by combining the two methods that is Content-based Filtering and item-based Collaborative Filtering. The merger of both of these methods are aimed at overcoming the weaknesses of each method. The algorithms used, namely Apriori on a Content-based Filtering and Cosine Similarity Centered on Collaborative Filtering. The experiment was performed on FilmKu information system by using Movielens dataset-100k. The results obtained in the form of a list of 10 recommendations of movies with a value of MAE and the ratio of the largest 0.8779 in ruleset reached 42%. Keywords: Apriori, Content-based Filtering, Centered Cosine Similarity, Itembased Collaborative Filtering, Lift Ratio, MAE, Mixed Hybrid, Movielens, Recommendation System.
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