A Framework for Tag-Based Research Paper Recommender System: An IR Approach

Jomsri, Pijitra; Sanguansintukul, Siripun; Choochaiwattana, Worasit

doi:10.1109/waina.2010.35

Cited by 36 publications

(31 citation statements)

References 26 publications

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“…Most participants in the reviewed user studies rated only a few recommendations and four studies (15 %) were conducted with fewer than five participants [62,123,171]; five studies (19 %) had five to ten participants [66,84,101]; three studies (12 %) had 11-15 participants [15,146,185]; and five studies (19 %) had 16-50 participants [44,118,121]. Six studies (23 %) were conducted with more than 50 participants [93,98,117].…”

Section: User Studiesmentioning

confidence: 99%

“…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]. CiteSeer made its corpus of research papers public, 23 as well as the citation graph of the articles, data for author name disambiguation, and the co-author network [290].…”

Section: Datasets and Architecturesmentioning

confidence: 99%

“…The approaches further extracted words from external sources, such as social tags [58,62], ACM classification tree and DMOZ categories [91,94], and citation context [51,55,65]. Using citation context is similar to the way search engines use anchor analysis for webpage indexing since the 1990s [320,322].…”

Section: Content-based Filteringmentioning

confidence: 99%

“…However, most approaches that inferred information automatically used all papers that a user authored, downloaded, etc. [62,94,122]. This is not ideal.…”

Section: Neglect Of User Modelingmentioning

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: User Studiesmentioning

confidence: 99%

Section: Datasets and Architecturesmentioning

confidence: 99%

Section: Content-based Filteringmentioning

confidence: 99%

“…However, most approaches that inferred information automatically used all papers that a user authored, downloaded, etc. [62,94,122]. This is not ideal.…”

Section: Neglect Of User Modelingmentioning

confidence: 99%

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Research-paper recommender systems: a literature survey

et al. 2015

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“…As, we have already mentioned the significance of this weighting factor .Let us take an example. Let us replace the terms with the tags in (8).If the values of (Tg_score * IDF score) is similar for the different tags, then weighting factor is used to differentiate the results. The tags with the higher weighting will be preferred as they have higher discriminating power for the category Ci in comparison to the tags having less weighting factor.…”

Section: Now Why We Have Included This Idf Score?mentioning

confidence: 99%

Semantic Web Improved with the Weighted IDF Feature

Gautam¹,

Kumar²

2015

ijacsa

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Abstract-The development of search engines is taking at a very fast rate. A lot of algorithms have been tried and tested. But, still the people are not getting precise results. Social networking sites are developing at tremendous rate and their growth has given birth to the new interesting problems. The social networking sites use semantic data to enhance the results. This provides us with a new perspective on how to improve the quality of information retrieval. As we are aware, many techniques of text classification are based on TFIDF algorithm. Term weighting has a significant role in classifying a text document. In this paper, firstly, we are extending the queries by "keyword+tags" instead of keywords only. In addition to this, secondly, we have developed a new ranking algorithm (JEKS algorithm) based on semantic tags from user feedback that uses CiteUlike data. The algorithm enhances the already existing semantic web by using the weighted IDF feature of the TFIDF algorithm. The suggested algorithm provides a better ranking than Google and can be viewed as a semantic web service in the domain of academics.

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KnowGen: A Knowledge Generation Approach for Tag Recommendation Using Ontology and Honey Bee Algorithm

Roopak

Deepak

2021

Artificial Intelligence Systems and the Internet of Things in the Digital Era

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A Framework for Tag-Based Research Paper Recommender System: An IR Approach

Cited by 36 publications

References 26 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Semantic Web Improved with the Weighted IDF Feature

KnowGen: A Knowledge Generation Approach for Tag Recommendation Using Ontology and Honey Bee Algorithm

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