On the recommending of citations for research papers

By Sean M. McNee, I. Albert, Dan Cosley, Prateep Gopalkrishnan, Shyong K. Lam, Al Mamunur Rashid, J. Konstan, John Riedl. 2002

455 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics, Information Retrieval & Search

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Citations in the corpus

Improving Wikipedia verifiability with AI (2022, background): “More broadly, citation retrieval and paper/source recommendation have also received attention in the scientific literature domain for many decades (McNee et al., 2002; Ren et al., 2014; Bhagavatula et al., 2018; Chou et al., 2020), albeit with less of a focus on verifiability of existing citations, ”
Support vector machines for collaborative filtering (2006, background): “In general, the content is difficult to analyze, thus CF algorithms are more successful in a broad range of areas, including recommending movies [20], news [3] and research papers [11].”
Context-aware citation recommendation (2010, methodology): “The previous work usually conducted user studies for this kind of relevance evaluation [ 16 , 6]. In this paper, we instead use the wisdom of the population as the ground truth to define a systematic metric.”
Evaluating recommender systems from the user’s perspective: survey of the state of the art (2012, methodology): “McNee et al. (2002) compared six recommendation algorithms through both offline and online experiments.”
Collaborative Filtering Recommender Systems (2011, background): “Recommendation algorithms with similar numeric performance have been known to return observably different results [97, 146], and a decrease in error may or may not make the system better at meeting the user’s needs.”
EventAction (2019, background): “[48] built a citation recommender system for research papers and measured the novelty of the recommended references to users.”
Evaluating Recommender Systems with User Experiments (2015, unknown): “[82] found that users rated their most accurate algorithm as least helpful, and Torres et al.”
Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment (2005, background): “The majority of scientific literature as well as other document retrieval systems (and many other systems) has been focusing on finding documents relative to users' interests, to name a few (Bollacker et al., 1999; McNee et al., 2002).”
Research-paper recommender systems: a literature survey (2015, methodology): “The TechLens team evaluated several content-based (CBF) and collaborative filtering (CF) approaches for research-paper recommendations.”
Evaluating collaborative filtering recommender systems (2004, background): “…at measures including the degree to which the recommendations cover the entire set of items [Mobasher et al. 2001], the degree to which recommendations made are non-obvious [McNee et al. 2002], and the ability of recommenders to explain their recommendations to users [Sinha and Swearingen 2002].”
Recommender systems: from algorithms to user experience (2012, methodology): “Similarly, our own TechLens research project has created research paper recommenders used for a series of one-shot experiments to assess recommendation algorithms for different user needs (Kapoor et al. 2007a,b,c; Torres et al. 2004; McNee et al. 2002; Ekstrand et al. 2010; McNee et al. 2006b).”
Explaining the user experience of recommender systems (2012, background): “In a comparison of six recommender algorithms, McNee et al. (2002) found that although the “Item-Item CF” algorithm provided the best predictions, users rated it the least helpful.”
Inspectability and control in social recommenders (2012, background): “[32] show that users are happy with a set of recommendations as long as it contains at least one novel item).”
A Neural Probabilistic Model for Context Based Citation Recommendation (2015, methodology): “McNee et al. (2002) used a partial list of reference as the input query and recommended additional references based on collaborative filtering.”
A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation (2013, background): “For instance, if a user writes a research paper and cites other articles, the citations are interpreted as positive votes of the cited articles [3].”
Content-Based Citation Recommendation (2018, methodology): “citeomatic/ 2012), or a partial list of citations provided by the author (McNee et al., 2002; Liu et al., 2015; Jia and Saule, 2017) which may not be available, e.”
Citation recommendation: approaches and datasets (2020, unknown): “In the following, we give such an overview.”