Abstract
Challenge in developing a collaborative filtering (CF)-based recommendation system is the problem of cold-starting 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.
Highlights
Mining on data of the small-medium enterprises (SME), is currently needed to improve their progress [1]
recommendation system (RS) can be applied in many fields, for example in e-commerce, RS is useful for recommending items that suit the interests and needs of users; In digital libraries, RS provides recommendations about books or other media that are relevant to user needs [3]
The collaborative filtering (CF) method must overcome the cold-start problem on new items that have not been rated by any user
Summary
Mining on data of the small-medium enterprises (SME), is currently needed to improve their progress [1]. Many data mining approaches can be used to explore and utilize the data, such as to make use the data in developing recommendation system (RS). RS is defined as an intelligent agent that advises users to find items that are more attractive to them, where users do not need to be involved with data stacks that are not related to their needs [2]. RS can be applied in many fields, for example in e-commerce, RS is useful for recommending items that suit the interests and needs of users; In digital libraries, RS provides recommendations about books or other media that are relevant to user needs [3]. The recommendation system is classified into three: Content-based-filtering, Collaborative-filtering, and the CF method must overcome the cold-start problem on new items that have not been rated by any user. [14] As a result, the data and the matrix representing the data become sparse and can result in decreased accuracy of the recommendations produced
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