On the recommending of citations for research papers

McNee, Sean M.; Albert, István; Cosley, Dan; Gopalkrishnan, Prateep; Lam, Shyong K.; Rashid, Al Mamunur; Konstan, Joseph A.; Riedl, John

doi:10.1145/587095.587096

Cited by 110 publications

(166 citation statements)

References 10 publications

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“…Six studies (23 %) were conducted with more than 50 participants [93,98,117]. Three studies (12 %) failed to mention the number of participants [55,61,149] (Table 4).…”

Section: User Studiesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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“…Six studies (23 %) were conducted with more than 50 participants [93,98,117]. Three studies (12 %) failed to mention the number of participants [55,61,149] (Table 4).…”

Section: User Studiesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…McNee [13] realized recommendation by building paper reference graph with collaborative filtering. Torres [14] combined collaborative filtering with content-based filtering.…”

Section: Related Workmentioning

confidence: 99%

“…The methods mentioned above mainly based on inter-citation by the historical papers [13] [18], or based on use of user's browse log [16][17] [14]. While modeling based on references between papers didn't take each researcher's interest into allsided consideration.…”

Section: Related Workmentioning

confidence: 99%

Personalized Paper Recommendation Based on User Historical Behavior

Wang

Liu

Dong

et al. 2012

Communications in Computer and Information Science

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Abstract. With the increasing of the amount of the scientific papers, it is very important and difficult for paper-sharing platforms to recommend related papers accurately for users. This paper tackles the problem by proposing a method that models user historical behavior. Through collecting the operations on scientific papers of online users and carrying on the detailed analysis, we build preference model for each user. The personalized recommendation model is constructed based on contentbased filtering model and statistical language model.. Experimental results show that users' historical behavior plays an important role in user preference modeling and the proposed method improves the final predication performance in the field of technical papers recommendation.

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“…Links to similar documents (based on text, co-citation, or bibliographic coupling) are offered in some repositories. Recommendations based on such local similarities have been found to be useful in suggesting new references (McNee et al, 2002).…”

Section: Motivation and Related Workmentioning

confidence: 99%

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

Berendt

Krause

Kolbe-Nusser

2010

Information Processing & Management

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Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.

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On the recommending of citations for research papers

Cited by 110 publications

References 10 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Personalized Paper Recommendation Based on User Historical Behavior

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

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