Date of Graduation

12-2013

Document Type

Thesis

Degree Name

Master of Science in Computer Science (MS)

Degree Level

Graduate

Department

Computer Science & Computer Engineering

Advisor

Susan E. Gauch

Committee Member

Brajendra Panda

Second Committee Member

Dale R. Thompson

Keywords

Communication and the arts; Applied sciences; Information retrieval; Recommender system

Abstract

CiteSeerx is a digital library for scientific publications by computer science researchers. It also functions as a search engine with several features including autonomous citation indexing, automatic metadata extraction, full-text indexing and reference linking. Users are able to retrieve relevant documents from the CiteSeerx database directly using search queries and will further benefit if the system suggests document recommendations to the user based on their preferences and search history. Therefore, recommender systems were initially developed and continue to evolve to recommend more relevant documents to the CiteSeerx users. In this thesis, we introduce the Conceptual, Impact-Based Recommender (CIBR), a hybrid recommender system, derived from the previously implemented conceptual recommender system in CiteSeerx. The Conceptual recommender system utilized the user's top weighted concepts to recommend relevant documents to the users. Our hybrid recommender system, CIBR, considers the impact factor in addition to the top weighted concepts for generating recommendations for the user. The impact factor of a document is determined by using the author's h-index of the publication. A survey was conducted to evaluate the efficiency of our hybrid system and this study shows that the CIBR system generates more relevant documents as compared to those recommended by the conceptual recommender system.

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