期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：10
页码：110-116
出版社：Science and Information Society (SAI)
摘要：Challenge in developing a collaborative filtering
(CF)-based recommendation system is the problem of coldstarting
of items that causes the data to sparse and reduces the
accuracy of the recommendations. Therefore, to produce high
accuracy a match is needed between the types of data and the
approach used. Two approaches in CF include user-based and
item-based CFs, both of which can process two types of data;
implicit and explicit data. This work aims to find a
combination of approaches and data types that produce high
accuracy. Cosine-similarity is used to measure the similarity
between users and also between items. Mean Absolute Error is
also measured to discover the accuracy of a recommendation.
Testing of three groups of data based on sparseness results in
the best accuracy in an explicit data-based approach that has
the smallest MAE value. The result is that the average MAE
value for user based (implicit data) is 0.1032, user based
(explicit data) is 0.2320, item based (implicit data) is 0.3495,
and item based (explicit data) is 0.0926. The best accuracy is in
the item-based (explicit-data) approach which is the smallest
average MAE value.
