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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Stefan Nikolich
Attacking the Cold Start Problem in Web-Advertising Service with Social-Network Data
2018
In recent years, number of Internet users that have been practicing the usage of various online stores or other Internet services is increasing. Thus, there is a growing demand for recommendation systems for those services. Since many types of implementations of those recommendation systems are heavily based on the history of interactions between users and objects, there are problems which often arise when this history does not exist. In such cases, services often use initial data about user. For example: the data specified when registering on the resource. However, in recent years an increasing number of users have been authorized into Internet services through various social networks. This opens a great potential for improving the recommendations to the above-described problematic users.

In this paper, we solve the problem of giving recommendations to such users on the basis of social network data for the service of advertisements. In particular, detecting the dependency between the user's data in the social network and his favorite category on the service of advertisements. A model of machine learning based algorithm using social network data (including the generated features) was constructed and results were obtained. Results are exceeding the random choice model but proved to be inferior to the existing model built on collaborative filtering.

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