Cascaded Feature Selection for Enhancing the Performance of Collaborative Recommender System

Most of collaborative recommender systems (CRSs) rely on statistical and data analysis methods for comparing users. However, dealing with them using machine learning techniques seems to be more appropriate. This paper investigates the usage of feature selection and classification methods for CRSs. It suggests building a user model suitable for the classification purpose and proposes a density-based feature selection (DBFS) method based on the rating density for each class. The DBFS reduces the effect of sparsity problem and keeps only users having a dense-feature history. Additionally, a cascaded feature selection method is proposed to pick out a subset of features through a two-layer approach. The first layer applies a classical feature selection method while the second layer applied the DBFS on the output of the first layer. The results show that the performance is gradually improved. The cascaded feature selection yields the best results since it improves the system accuracy, reduces the space and processing complexities, and alleviates the sparsity in two cascaded layers. The achieved improvements by cascaded feature selection as compared to SVM are 6.55 percent, 10.14 percent, and 3.92 percent in terms of accuracy, F-measure and MAE, respectively

An effective location-based information filtering system on mobile devices

As mobile devices evolve, research on providing location-based services attract researchers interest. A location-based service recommends information based on users geographical location provided by a mobile device. Mobile devices are engaged with users daily activities and lots of information and services are requested by users, so suggesting the proper information on mobile devices that reflects user preferences becomes more and more difficult. Lots of recent studies have tried to tackle this issue but most of them are not successful because of reasons such as using large datasets or making suggestions based on dynamically collected ratings within different groups instead of focusing on individuals. In this paper, we propose a location based information filtering system that exposes users preferences using Bayesian inferences. A Bayesian network is constructed with conditional probability table while Users characteristics and location data are gathered by using the mobile device. After preprocessing those data, the system integrates that information and uses time to produce the most accurate suggestions. We collected a dataset from 20 restaurants in Malaysia and we gathered behavioral data from two registered users for 7 days. We conducted experiment on the dataset to demonstrate effectiveness of the proposed system and to explain user preferences