Exploring the Potential of User Modeling Based on Mind Maps

Beel, Joeran; Langer, Stefan; Kapitsaki, Georgia M.; Breitinger, Corinna; Gipp, Béla

doi:10.1007/978-3-319-20267-9_1

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…In the domain of research-paper recommender systems, only Beel et al applied stereotypes [311,312]. The authors assume that all users of their reference-management software Docear are researchers or students.…”

Section: Stereotypingmentioning

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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Section: Stereotypingmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…In another experiment, effectiveness was almost the same (CTR = 5.94 % vs. CTR = 6.31 %) (Beel and Langer 2015). Similarly, in one experiment, the 'stereotype' recommendation approach was around 60 % more effective than in another experiment (CTR = 3.08 % vs. CTR = 4.99 %) (Beel et al 2014a(Beel et al , 2015b.…”

Section: Introductionmentioning

confidence: 73%

“…We chose the stereotype approach mainly as a baseline and to have an approach that was fundamentally different from content-based filtering. For a detailed overview of the recommender system's architecture and algorithms please refer to (Beel2015; Beel et al 2014bBeel et al , 2015b. There are three types ofDocear users: registered users, local users, and anonymous users (Beel et al 2013g).…”

Section: Methodsmentioning

confidence: 99%

Towards reproducibility in recommender-systems research

Beel

Breitinger

Langer

et al. 2016

User Model User-Adap Inter

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Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system research. We conduct experiments using Plista's news recommender system, and Docear's research-paper recommender system. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. For example, in one newsrecommendation scenario, the performance of a content-based filtering approach User-Adapted Interaction : umuai ; 26 (2016), 1. -S. 69-101 https://dx.doi.org/10.1007 was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. Some of the determinants have interdependencies. For instance, the optimal size of an algorithms' user model depended on users' age. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach's performance, ensuring reproducibility of experimental results is difficult. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research.

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“…Apparently, traditional recommender systems are punier to recommend most suitable research resources to the scholars which may cause wackness emanating from irrelevant results due to the cold-start problem [3][4][5] and ranking sparsity problem [6,7]. To elucidate this issue, several recommendation methods [8,9,[18][19][20][21][22][23][24][10][11][12][13][14][15][16][17] have recently appeared to provide Scholars with more appropriate and suitable results. These recommendation methods sort out results according to the interests of Scholars by filtering appropriate and inappropriate results.…”

Section: N T R O D U C T I O Nmentioning

confidence: 99%

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

Amaad

Jhamat

Riaz³

et al. 2020

jictra

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The availability of huge volumes of online research papers over scholarly communities has been increasing rapidly with the evolution of the Internet. Meanwhile, several researchers confront troubles while retrieving suitable and relevant research papers according to their research necessities due to information overload. Besides, the research necessities vary from researcher to researcher according to their contextual state and the online behavior in sequential access. Conventional recommendation approaches for instance content-based filtering (CBF) and collaborative filtering (CF) utilize content features and rankings correspondingly, in order to produce recommendations for the researchers. In spite of this, it is inevitable to incorporate scholar’s contextual information and sequential access behavior into the recommendation procedure to generate accurate and personalized recommendations for research papers. Conventional recommender systems do not incorporate such information in the recommended procedure to compute similarities of scholars and provide recommendations; thus, they are more liable to produce an irrelevant list of recommendations in a scholarly environment. Moreover, conventional recommendation approaches generate inaccurate recommendations in presence of a high level of sparsity in the rankings. In this article, we introduce a novel method for research paper recommendations that incorporates the benefits of collective filtering (CF), context-awareness, and sequential pattern mining (SPM) to propose research papers to scholars in a hybrid manner. Context-awareness in our methodology involves the scholar's contextual state, such as skill level and research goals; SPM is used to mine weblogs and reveal sequential access actions of scholars, and CF is used to measure predictions based on correlations between scholars and generate context-aware and sequential trend mining based recommendations for the targeted scholars. Experimental evaluations of our approach indicate the excellence of our approach over other baseline approaches in terms of precision, recall, F1, and mean absolute error (MAE).

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Exploring the Potential of User Modeling Based on Mind Maps

Cited by 10 publications

References 13 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Towards reproducibility in recommender-systems research

Context-Aware and Sequential Pattern Mining based recommendations for Research Papers: A Hybrid Approach

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