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

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

University of Amsterdam at TAC 2012

Title Context-Based Entity Linking – University Of Amsterdam At TAC 2012 [link]
Author Graus, D.P., Kenter, T.M., Bron, M.M., Meij, E.J., de Rijke, M.
Publication type Conference Proceedings
Conference name Text Analysis Conference 2012
Conference location Gaithersburg, MD
Abstract This paper describes our approach to the 2012 Text Analysis Conference (TAC) Knowledge Base Population (KBP) entity linking track. For this task, we turn to a state-of-the-art system for entity linking in microblog posts. Compared to the little context microblog posts provide, the documents in the TAC KBP track provide context of greater length and of a less noisy nature. In this paper, we adapt the entity linking system for microblog posts to the KBP task by extending it with approaches that explicitly rely on the query’s context. We show that incorporating novel features that leverage the context on the entity-level can lead to improved performance in the TAC KBP task.
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Abstract

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