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Recommender Systems Based on Community Structure among Users and Items

Khademi, Ehsan | 2014

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 46661 (19)
  4. University: Sharif University Technology
  5. Department: Computer Engineering
  6. Advisor(s): Jalili, Mahdi
  7. Abstract:
  8. Mankind with it’s finite resources (Time, Energy, …) cannot make use of every accessible option in daily activities (such as buying items, listening to music and reading news), and is restricted to decide on a handful of them. Available options are increasing on a daily basis and these surplus of available options had an adverse effect; Thus, leading us to more baffling situations. As a result, need for external assistance appeared in decision situations. Considering exceptional computation power available to computers, a framework named Recommender Systems were developed. Recommender systems try to use their accessible data in order to make fitting suggestions to users. Personalization and usefulness are two of the principal attributes regarding the recommendations. In recommender systems, there exists two types of entities: Users and Items. Bipartite network is the structure used to model these type of data. In this work, we try to discover the underlying community structure of the bipartite network. Two main approaches for detecting community structures were examined as a means to find the appropriate one in this application.In addition, a recommender system is proposed which uses the community structure for recommendation list generation. Reported results indicate that in recommender system applications, it is not required to apply direct approaches for the sake of community extraction; One can project it to it’s equal one-mode network and the information loss in the process is negligible. Found communities could be stored for future use. Furthermore, the recommender system based on community structure acquired high accuracy in conducted experiments and could be utilized in large scale applications
  9. Keywords:
  10. Complex Network ; Machine Learning ; Recommender System ; Community Detection ; Bipartite Networks ; Network Projection

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