Abstract
One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.
Highlights
The increasing volume and complexity of online information make it difficult for users to obtain appropriate information
Several studies performed improvement of traditional similarity metrics to increase the performance of recommendation systems
The results showed that combining memory-based and clustering methods could improve the prediction performance by reducing Mean Absolute Error (MAE) by 1.88% and Root Mean Square Error (RMSE) by 1.46% compared to the baseline method (UPCSim)
Summary
The increasing volume and complexity of online information make it difficult for users to obtain appropriate information. The recommendation system is the ultimate solution to deal with the information explosion [1,2]. This system is a valuable information filtering tool to assist users in finding a product or service from the many possibilities that exist. One of the most prevalent approaches to recommendation systems is collaborative filtering [5,6,7,8] This approach is capable of generating recommendations based on the ratings provided by the users for several items. The first method uses a model built from the ratings to generate recommendations, while the second method utilizes similarity metrics to get the distance between two users/items [6,9]
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