Outline

  • Abstract
  • Keywords
  • I. Introduction
  • II. Related Work
  • III. Network Theory
  • IV. Social Genome
  • V. Link Recommendation
  • VI. Experimental Results
  • VII. Conclusion and Future Work
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1. مقدمه
  • 2. پژوهش‌های مرتبط
  • 3. نظریۀ شبکه
  • 4. ژنوم اجتماعی
  • 5. توصیه لینک
  • 6. نتایج تجربی
  • 7. نتیجه‌گیری و پژوهش‌های آتی

Abstract

Social networking sites employ recommendation systems in contribution to providing better user experiences. The complexity in developing recommendation systems is largely due to the heterogeneous nature of social networks. This paper presents an approach to friend recommendation systems by using complex network theory, cognitive theory and a Pareto-optimal genetic algorithm in a two-step approach to provide quality, friend recommendations while simultaneously determining an individual’s perception of friendship. Our research emphasizes that by combining network topology and genetic algorithms, better recommendations can be achieved compared to each individual counterpart. We test our approach on 1,200 Facebook users in which we observe the combined method to outper form purely social or purely network-based approaches. Our preliminary results represent strong potential for developing link recommendation systems using this combined approach of personal interests and the underlying network.

Keywords: - - - -

Conclusions

We presented a method for friend recommendation systems in social networks to address the problem of determining how and why links are formed within social networks.

By addressing this problem with support from complex network theory, cognitive theory and genetic algorithms, our claim is that the combination of social9based and network9 based approaches is more effective in recommendation com9 pared to its individual counterparts. In this paper, we developed a friend recommendation system that produced quality, rele9 vant friend recommendations in addition to providing insights into each individual’s perception of friendship. This method has shown that a combined approach has thus far outperformed purely social and purely network9based approaches but still has much room for improvement. The primary issue attributing to lower performance in social9based approaches is due largely to the completeness of data. In order for social9based approaches to thrive, it is important to work with users whom expose more information on these social networks. Additionally, social9 based approaches will perform better if user information is truthful. Our methodology and results in this paper presents initial findings to a potentially strong method of providing friend recommendations in social networks while additionally gaining insights into how friendships are established.

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