Anil Utku

A weighted multi-attribute-based recommender system using extended user behavior analysis

Abstract A new weighted multi-attribute based recommender system (WMARS) has been developed using extended user behavior analysis. WMARS obtained data from number of clicked items in the recommendation list, sequence of the clicked items in recommendation the list, duration of tracking, number of tracking same item, likes/dislikes, association rules of clicked items, remarks for items. WMARS has been applied to a movie web site. The experimental results have been obtained from a total of 567 heterogeneous users, including employers in different sectors, different demographic groups, and undergraduate and graduate students. Using different weighted sets of the attributes’ parameters, WMARS has been tested and compared extensively with collaborative filtering. The experimental results show that WMARS is more successful than collaborative filtering for the data set that was used.

Identification of shares containing offensive charge in social media

The increasing use of social media brings with it several problems. The usage of insulting content on social networks, for some people or institutions, constitutes crimes. In this case, the study is about analyzing text content shared by users in the social media with the help of Naive Bayes Method and Vector Space Model from Machine Learning methods, and whether or not these shares contain insults, if any, and at what rate they contain.

Malware detection system based on machine learning methods for Android operating systems

Mobile devices begin to spread increasingly recently to offer a lot of services which personal computers offer. This condition has led to increase in the number of security threats in mobile devices and services. In this paper, it has been made a research on mobile malware and malware detection techniques. Within the scope of the study, a a permission based detection system based on the machine learning methods for Android malware was developed. Developed system is analyzed by using Random Forest, Support Vector Machine and Artificial Neural Networks algorithms.

A New Recommender System Based on Multiple Parameters and Extended User Behavior Analysis

In this paper, a new recommender system has been developed based on extended user behavior analysis. The proposed system extracts information from recall, precision, number of clicked items, sequence of the clicked items, duration of tracking, number of tracking same item, like/unlike, association rules of clicked items, and remarks for items. The developed recommender system has been applied to a movie web site and experimental results have been obtained from undergraduate and graduate students at Gazi University, Ankara, Turkey. The proposed recommender system has been tested and compared extensively with the collaborative filtering. The experimental results show that the developed recommender system is more successful than collaborative filtering.