Statistical Implicative Similarity Measures for User-based Collaborative Filtering Recommender System

Abstract

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.

Context-Similarity Collaborative Filtering Recommendation

“…Evaluation based on predicted rating value of models is a method based on the accuracy of the models by comparing the predicted rating value with the actual rating value [31]. This method often uses the five measures used to calculate errors in statistics: Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE).…”

“…It is considered good when quantities of these errors are of low values [12], [15]. [25], [31]: The average square error between user rating value and model's predicted rating value.…”

“…Root Mean Square Error (RMSE) [25], [31]: The square root of the mean square error between user rating value and the model's predicted rating value.…”

“…Mean Absolute Error (MAE) [25], [31]: The average absolute error between user rating value and model's predicted rating value. (6) where κ is the number rating values; r ij is the actual rating value for item i by user i;r ij is the predicted value of the models;…”

Graph-Based Collaborative Filtering with MLP

“…The model-based CF is different from memory-based CF [8]. The model-based methods learn from statistical model [9], using machine learning methods [10] to learn the features and train a model. Then we use the previous trained model to predict the rating of items that have not been rated previously.…”