Enhancing digital libraries with TechLens+

Torres, Roberto; McNee, Sean M.; Abel, Mara; Konstan, Joseph A.; Riedl, John

doi:10.1145/996350.996402

Cited by 181 publications

(150 citation statements)

References 10 publications

Supporting

Mentioning

144

Contrasting

Unclassified

Order By: Relevance

“…Consequently, some users might be interested in novel research-paper recommendations, while others might be interested in authoritative research-paper recommendations. Of course, users require recommendations specific to their fields of research [117]. When we use the term "effectiveness," we refer to the specific objective the evaluator wanted to measure.…”

Section: %mentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

View full text Add to dashboard Cite

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...

show abstract

Section: %mentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

View full text Add to dashboard Cite

show abstract

“…McNee [13] realized recommendation by building paper reference graph with collaborative filtering. Torres [14] combined collaborative filtering with content-based filtering. Since Torres accepted recommendation results from other systems before filtering, it was difficult to implement such input, thus preventing it from being applied to practical applications.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…However, full-text of academic literature is often costly or difficult to find, recommender for scientific articles such as TechLens [1] are not even close to the quality of music and movie recommender such as Last.fm and Netflix, and researchers having read and annotated hundreds of papers will easily lose track of what was written in which paper .…”

Section: Introductionmentioning

confidence: 99%

Docear

Beel¹,

Gipp²,

Langer³

et al. 2011

Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries

View full text Add to dashboard Cite

In this demonstration-paper we introduce Docear, an 'academic literature suite'. Docear offers to scientists what an office suite like Microsoft Office offers to office workers. While an office suite bundles various applications for office workers (word processing, spreadsheets, presentation software, etc.), Docear bundles several applications for scientists: academic search engine, PDF reader, reference manager, word processor, mind mapping module, and recommender system. Besides Docear's general concept, its special features are presented in this paper, namely a modular composition, free full-text access to literature, information management as mind map, automatic metadata extraction of PDFs and recommendations.

show abstract

Enhancing digital libraries with TechLens+

Cited by 181 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

Docear

Contact Info

Product

Resources

About