Citation Recommendation

Direction Awareness in Citation Recommendation

Onur Kucuktunc, Erik Saule, Kamer Kaya, Umit V. Catalyurek

Abstract - Literature search is an important part of academic research. The increase in the number of published papers each year makes manual search inefficient, hence, automatic methods must be devised. Unfortunately, traditional search engines use keyword-based approaches to solve the search problem which are prone to ambiguity and synonymity. This paper focuses on the problem of extending a set of references using the citation relations between the documents. In particular, we introduce the class of direction-aware algorithms which weight the importance of incoming and outgoing edges of the citation graph differently based on user preferences. Using such an algorithm, the user can easily focus her search toward recent developments or traditional papers. We present two direction-aware algorithms and show that they are better suited at solving the problem at hand than state-of-the-art recommendation methods. One of these algorithms is currently deployed in a publicly available web-service called theadvisor.

N/A
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literature search, graph, random walks, paper recommendation, web service

Supplementary Material

theadvisor is a graph-based citation, venue, and reviewer recommendation service developed by researchers of High Performance Computing Lab at The Ohio State University.

Fast Recommendation on Bibliographic Networks

Onur Kucuktunc, Kamer Kaya, Erik Saule, Umit V. Catalyurek

Abstract - Graphs and matrices are widely used in algorithms for social network analyses. Since the number of interactions is much less than the possible number of interactions, the graphs and matrices used in the analyses are usually sparse. In this paper, we propose an efficient implementation of a sparse-matrix computation which arises in a publicly available citation recommendation service called theadvisor. The recommendation algorithm uses a sparse matrix generated from the citation graph. We observed that the nonzero pattern of this matrix is highly irregular and the computation suffers from high number of cache misses. We propose techniques for storing the matrix in memory efficiently and reducing the number of cache misses. Experimental results show that our techniques are highly efficient on reducing the query processing time which is highly crucial for a web service.

10.1109/ASONAM.2012.82
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citation recommendation, social network analysis, sparse matrices, hypergraphs, cache locality

Supplementary Material

Recommendation on Academic Networks using Direction Aware Citation Analysis

Onur Kucuktunc, Erik Saule, Kamer Kaya, Umit V. Catalyurek

Abstract - The literature search has always been an important part of an academic research. It greatly helps to improve the quality of the research process and output, and increase the efficiency of the researchers in terms of their novel contribution to science. As the number of published papers increases every year, a manual search becomes more exhaustive even with the help of today's search engines since they are not specialized for this task. In academics, two relevant papers do not always have to share keywords, cite one another, or even be in the same field. Although a well-known paper is usually an easy pray in such a hunt, relevant papers using a different terminology, especially recent ones, are not obvious to the eye.
In this work, we propose paper recommendation algorithms by using the citation information among papers. The proposed algorithms are direction aware in the sense that they can be tuned to find either recent or traditional papers. The algorithms require a set of papers as input and recommend a set of related ones. If the user wants to give negative or positive feedback on the suggested paper set, the recommendation is refined. The search process can be easily guided in that sense by relevance feedback. We show that this slight guidance helps the user to reach a desired paper in a more efficient way. We adapt our models and algorithms also for the venue and reviewer recommendation tasks. Accuracy of the models and algorithms is thoroughly evaluated by comparison with multiple baselines and algorithms from the literature in terms of several objectives specific to citation, venue, and reviewer recommendation tasks. All of these algorithms are implemented within a publicly available web-service framework (theadvisor) which currently uses the data from DBLP and CiteSeer to construct the proposed citation graph.

arXiv:1205.1143
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literature search, graph, random walks, paper recommendation, web service

Supplementary Material