Abstract
This paper proposes a new similarity measures for User-based collaborative filtering recommender system. The similarity measures for two users are based on the Implication intensity measures. It is called statistical implicative similarity measures (SIS). This similarity measures is applied to build the experimental framework for User-based collaborative filtering recommender model. The experiments on MovieLense dataset show that the model using our similarity measures has fairly accurate results compared with User-based collaborative filtering model using traditional similarity measures as Pearson correlation, Cosine similarity, and Jaccard.
Highlights
Recommender system is considered as a useful tool for solving partial information overload of the Internet [3][12]
1) Evaluation based on the ratings the error parameters (RMSE, Mean squared error (MSE), Mean absolute error (MAE)) are calculated for each user and for the model based on the data which is built by k-fold method
We built User-based collaborative filtering recommender model by suggesting a new similarity measures based on Implications intensity measures in order to determine the similarity of two users
Summary
Recommender system is considered as a useful tool for solving partial information overload of the Internet [3][12]. The third generation of recommender systems is developed in parallel with the web 3.0 with information collected from integrated devices on the Internet such as cameras, sensors [22] This generation uses approaches to integrate location information into the available recommendation algorithms in order to broaden its application in various fields such as health, weather, environment, and universe [1]. Some new research directions are set out, such as research on proper combination of existing recommendation methods that use different types of available information; research on using the maximum capabilities of the sensors and devices on the Internet; research on collecting and integrating information on trends related to habits, consumption and individual tastes of users in the recommendation process; research on ensuring the security conditions and privacy in the entire process of recommendation system; research on proposing the measures for evaluating recommender systems and develop a standard for assessment measures and research on developing a framework for automated analysis on heterogeneous data. The final section summarizes some importantly achieved results of model using similarity measures between two users based on Implication intensity measures
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