Concept-Based Document Recommendations for CiteSeer Authors

Chandrasekaran, K.; Gauch, Susan; Lakkaraju, Praveen; Luong, Hiep

doi:10.1007/978-3-540-70987-9_11

Cited by 51 publications

(28 citation statements)

References 17 publications

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“…CiteULike 21 and Bibsonomy 22 published datasets containing the social tags that their users added to research articles. The datasets were not originally intended for recommender-system research but are frequently used for this purpose [56,62,112].…”

Section: Datasets and Architecturesmentioning

confidence: 99%

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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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Section: Datasets and Architecturesmentioning

confidence: 99%

“…The datasets include 20 http://trec.nist.gov/data.html. 21 http://www.citeulike.org/faq/data.adp. 22 https://www.kde.cs.uni-kassel.de/bibsonomy/dumps/.…”

Section: Datasets and Architecturesmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…It replaces the previous 1998 version of the ACM CCS (the '98 ACM CSS'), which has served as the de facto standard classification system for the computing field, and has been used by several recent recommender systems (e.g. [3], [8], [9]). The 98 ACM CCS ontology has a three-level hierarchical set of concepts that contains in total 369 concepts [8].…”

Section: Introductionmentioning

confidence: 99%

“…With a complex ontology, there is a need to employ more sophisticated techniques to build a user 978-1-5090-5476-3/17/$31.00 ©2017 IEEE profile. The tree of concepts model [3] used in conjunction with complex ontologies addresses this problem, but it is static, in that it is unable to dynamically capture new and multiple user's interests. Furthermore, it does not normalize the user model according to the number of papers that are involved in the user profile, which causes its performance to decline significantly if the profile contains larger numbers of papers.…”

Section: Introductionmentioning

confidence: 99%

A research paper recommender system using a Dynamic Normalized Tree of Concepts model for user modelling

Alshaikh

Uchyigit

Evans

2017

2017 11th International Conference on Research Challenges in Information Science (RCIS)

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Abstract-The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.

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“…Chandrasekaran et al (2008), for example, present a method of recommending scientific papers of potential interest to users by using the ACM Computing Classification System along with hierarchical concept information from both author profiles and paper content. Based on this work, He et al (2010) proposed a method to recommend global and local citations based on a piece of given text under both context-oblivious and context-aware conditions.…”

Section: Citation Recommendationmentioning

confidence: 99%

Citation Recommendation via Proximity Full-Text Citation Analysis and Supervised Topical Prior

Liu¹,

Zhang²,

Guo³

iConference 2016 Proceedings

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Currently the many publications are now available electronically and online, which has had a significant effect, while brought several challenges. With the objective to enhance citation recommendation based on innovative text and graph mining algorithms along with full-text citation analysis, we utilized proximitybased citation contexts extracted from a large number of full-text publications, and then used a publication/citation topic distribution to generate a novel citation graph to calculate the publication topical importance. The importance score can be utilized as a new means to enhance the recommendation performance. Experiment with full-text citation data showed that the novel method could significantly (p < 0.001) enhance citation recommendation performance.

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Concept-Based Document Recommendations for CiteSeer Authors

Cited by 51 publications

References 17 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

A research paper recommender system using a Dynamic Normalized Tree of Concepts model for user modelling

Citation Recommendation via Proximity Full-Text Citation Analysis and Supervised Topical Prior

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