A study on the accuracy of prediction in recommendation system based on similarity measures

Abstract EN

Recommender Systems are tools to understand the huge amount of data available in the internet world.

Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system.

Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user.

Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations.

In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix.

The advantages and disadvantages of each measure are spotted.

From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings.

After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.