Recommender System for Bibliographic Citations.

Date of Submission

December 2014

Date of Award

Winter 12-12-2015

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Computer Vision and Pattern Recognition Unit (CVPR-Kolkata)


Mitra, Mandar (CVPR-Kolkata; ISI)

Abstract (Summary of the Work)

While writing research papers, we wish to find the best possible references for what we have written in our paper. Finding them manually is both time consuming and difficult. A citation recommender system takes a research paper draft as input and outputs citation recommendations. The recommender’s job is challenging as the recommendation should not only be relevant to the paper in general, but also should be relevant to the local context of the paper in composition.1.1 Introduction to Recommender SystemsRecommender systems is one of the fields which has grown in parallel to the web. It is also a field that grew out of necessity, as the amount of information available on the web has become increasingly enormous. John Naisbitt once said: “We are drowning in information but starved for knowledge.”[9] So, it is important to have good technologies that can translate information to knowledge. One such technology that has become successful is Recommender Systems. M. Deshpande and G. Karypis defined Recommender Systems as: “a personalized information filtering technology used to either predict whether a particular user will like a particular item (prediction problem) or to identify a set of N items that will be of interest to a certain user (top-N recommendation problem)”[3]There are many approaches to build Recommender Systems. These approaches are typically classified as follows :• Content-based : Recommendations are selected based on the target user’s previously liked content.• Collaborative Filtering : Recommendations are selected based on items liked by other users with similar tastes and preferences.• Hybrid approaches : They combine Collaborative Filtering and Content Based Methods.1.2 Introduction to Citation recommender systemsCurrent citation recommender systems can broadly be classified into three categories.• The first category of recommenders try to complete the citation list of an input text. Here, some of the citations are already specified by the author. For example, McNee et al proposed an approach using collaborative filtering that falls into this category. Their algorithm analyses the citation graph and builds ratings. The details of this algorithm are discussed in the next chapter[10].• The second category of recommenders receive just a text as input and generate recommendations from them. For example, Strohman et al. used a two-step recommendation algorithm. They first generated a candidate list of recommendations using the content and citation graph and in the second step, they ranked these recommendations[14].• The third category of recommenders, placeholders, ie places where citations should be added, are also specified in the text. For example, He et al proposed an approach which proposed recommendations for specified locations[4].Our recommender falls into the third category


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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