A Qualitative Evaluation of User Preference for Link-based vs. Text-based Recommendations of Wikipedia Articles

  title={A Qualitative Evaluation of User Preference for Link-based vs. Text-based Recommendations of Wikipedia Articles},
  author={Malte Ostendorff and Corinna Breitinger and Bela Gipp},
Literature recommendation systems (LRS) assist readers in the discovery of relevant content from the overwhelming amount of literature available. Despite the widespread adoption of LRS, there is a lack of research on the user-perceived recommendation characteristics for fundamentally different approaches to content-based literature recommendation. To complement existing quantitative studies on literature recommendation, we present qualitative study results that report on users’ perceptions for… 

Figures and Tables from this paper

Scientific Paper Recommendation Systems: a Literature Review of recent Publications
This literature review discusses used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021 to provide a comprehensive and complete overview of current paper recommendation systems.


Evaluating link-based recommendations for Wikipedia
The results show that the citation-based measures CPA and CoCit have complementary strengths compared to the text-based MLT measure, which is better suited for identifying a broader spectrum of related articles, as well as popular articles that typically exhibit a higher quality.
Research-paper recommender systems: a literature survey
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 aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
Can we do better than Co-Citations? - Bringing Citation Proximity Analysis from idea to practice in research article recommendation
This paper builds on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems, and introduces three new proximity functions.
Looking for "Good" Recommendations: A Comparative Evaluation of Recommender Systems
An empirical study that involved 210 users and considered seven RSs on the same dataset that use different baseline and state-of-the-art recommendation algorithms was discussed, measuring the user's perceived quality of each of them, focusing on accuracy and novelty of recommended items, and on overall users' satisfaction.
A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
There is a strong correlation between participant scores and the similarity rankings produced by the large scale hybrid paper recommender system but that additional focus needs to be put towards improving recommender precision, particularly for content based recommendations.
Towards reproducibility in recommender-systems research
The recommender-system community needs to survey other research fields and learn from them, find a common understanding of reproducibility, identify and understand the determinants that affect reproduCibility, conduct more comprehensive experiments, and establish best-practice guidelines for recommender -systems research.
Evaluating document representations for content-based legal literature recommendations
A set of state-of-the-art document representation methods for the task of retrieving semantically related US case law are explored and it is shown that document representations from averaged fastText word vectors yield the best results, closely followed by Poincaré citation embeddings.
Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems
It is suggested that RS should provide richer explanations in order to increase their perceived recommendation quality and trustworthiness.
Evaluating content novelty in recommender systems
The findings demonstrate that the proposed measures yield consistent and interpretable results, producing insights that reduce the impact of popularity bias in the evaluation of recommender systems.
Explaining the user experience of recommender systems
This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively).