Reading News with a Purpose: Explaining User Profiles for Self-Actualization

📅 April 10, 2019 • 🕐 11:15 • 🏷 Papers • 👁 150

Really excited to have co-authored “Reading News with a Purpose,” which was accepted at the International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data (ExHUM), at UMAP 2019!

With the largest list of authors (ranging from philosophers via polcomm researchers to computer scientists), from a wide array of institutions; Emily Sullivan, Dimitrios Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, Mykola Makhortykh, Domokos Kelen, Darcia Wilkinson, and Nava Tintarev!

This is work that came out of our ICT with Industry project “Opening the black box of user profiles in content-based recommender systems” where we (FD Mediagroep) collaborated with Nava Tintarev and our excellent team of academics in a week-long academic hackathon!

Read the pre-print, below:

  • [PDF] E. Sullivan, D. Bountouridis, J. Harambam, S. Najafian, F. Löcherbach, M. Makhortykh, D. Kelen, D. Wilkinson, D. Graus, and N. Tintarev, “Reading news with a purpose: explaining user profiles for self-actualization,” in Proceedings of 27th conference on user modeling, adaptation and personalization adjunct, 2019.
    [Bibtex]
    @inproceedings{sullivan2019reading,
    title={Reading News with a Purpose: Explaining User Profiles for Self-Actualization},
    author={Sullivan, Emily and Bountouridis, Dimitrios and Harambam, Jaron and Najafian, Shabnam and Löcherbach, Felicia and Makhortykh, Mykola and Kelen, Domokos and Wilkinson, Daricia and Graus, David and Tintarev, Nava},
    booktitle={Proceedings of 27th Conference on User Modeling, Adaptation and Personalization Adjunct},
    year={2019},
    organization={ACM}
    }

Read the original idea that sparked the project, presented at the 2nd FATREC Workshop at RecSys 2018, here:

  • [PDF] D. Graus, M. Sappelli, and D. M. Chu, ““let me tell you who you are” — explaining recommender systems by opening black box user profiles,” in The 2nd fatrec workshop on responsible recommendation, 2018.
    [Bibtex]
    @inproceedings{graus2018let,
    title={“Let Me Tell You Who You are” — Explaining Recommender Systems by Opening Black Box User Profiles},
    author={Graus, David and Sappelli, Maya and Chu, Dung Manh},
    booktitle={The 2nd FATREC Workshop on Responsible Recommendation},
    year={2018}
    }

Position paper ““Let Me Tell You Who You are” — Explaining Recommender Systems by Opening Black Box User Profiles”

📅 November 11, 2018 • 🕐 11:21 • 🏷 Papers • 👁 39

Our position paper ““Let Me Tell You Who You are” — Explaining Recommender Systems by Opening Black Box User Profiles” was accepted at the 2nd FATREC Workshop on Responsible Recommendation, held at RecSys ’18!

In this paper, we detail some our ideas and approaches of providing transparency in recommendations through displaying the user profiles, used ‘internally’ by our recommender system. Read the pre-print below!

  • [PDF] D. Graus, M. Sappelli, and D. M. Chu, ““let me tell you who you are” — explaining recommender systems by opening black box user profiles,” in The 2nd fatrec workshop on responsible recommendation, 2018.
    [Bibtex]
    @inproceedings{graus2018let,
    title={“Let Me Tell You Who You are” — Explaining Recommender Systems by Opening Black Box User Profiles},
    author={Graus, David and Sappelli, Maya and Chu, Dung Manh},
    booktitle={The 2nd FATREC Workshop on Responsible Recommendation},
    year={2018}
    }
FATREC Position paper: Explaining recommender systems by opening black box user profiles

Abstract

📅 March 10, 2012 • 🕐 14:10 • 🏷 Thesis (MSc) • 👁 42

Below the first draft of the abstract of my paper. It doesn’t yet include the results/conclusion. Word count: 127

Semantic annotation uses human knowledge formalized in ontologies to enrich texts, by providing structured and machine-understandable information of its content. This paper proposes an approach for automatically annotating texts of the Cyttron Scientific Image Database, using the NCI Thesaurus ontology. Several frequency-based keyword extraction algorithms, aiming to extract core concepts and exclude less relevant concepts, were implemented and evaluated. Furthermore, text classification algorithms were applied to identify important concepts which do not occur in the text. The algorithms were evaluated by comparing them to annotations provided by experts. Semantic networks were generated from these annotations and an ontology-based similarity metric was used to cross-compare them. Finally the networks were visualized to provide further insights into the differences of the semantic structure generated by humans, and the algorithms.

Tags: Semantic annotation, ontology-based semantic similarity, semantic networks, keyword extraction, text classification, network visualization, text mining