Abstract
This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.
Highlights
The information overload problem on the Internet is popular today so that the recommendation system is powerful methods to handle these problems
We propose the OurMovieSimilarity (OMS) is the crowdsourcing system which can be collecting the cognitive similarity of users
We proposed the three-layered architecture which can extract the cognitive similarity so that it can exactly identify the k-nearest neighbors
Summary
The information overload problem on the Internet is popular today so that the recommendation system is powerful methods to handle these problems. CF exploits the similarities between ranking behaviors of users
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