1. 2019
  2. A methodology for creating and validating psychological stories for conveying and measuring psychological traits

    Smith, K. A., Dennis, M., Masthoff, J. & Tintarev, N., 2019, In : User Modeling and User-Adapted Interaction.

    Research output: Contribution to journalArticleScientificpeer-review

  3. Does reviewer recommendation help developers?

    Kovalenko, V., Tintarev, N., Pasynkov, E., Bird, C. & Bacchelli, A., 2019, (Accepted/In press) In : IEEE Transactions on Software Engineering. p. 1-23 23 p.

    Research output: Contribution to journalArticleScientificpeer-review

  4. 2018
  5. A Diversity Adjusting Strategy with Personality for Music Recommendation

    Lu, F. & Tintarev, N., 2018, In Recsys workshop on Interfaces and Decision Making in Recommender Systems.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  6. Digitally Scaffolding Debate in the Classroom.

    Holzer, A., Tintarev, N., Bendahan, S., Greenup, S. & Gillet, D., 2018, Proceedings of the 2018 CHI Conference Extended Abstracts on Human Factors in Computing Systems..

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  7. Effects of Individual Traits on Diversity-aware Music Recommender User Interfaces

    Jin, Y., Tintarev, N. & Verbert, K., 2018, UMAP.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  8. Effects of Personal Characteristics on Music Recommender Systems with Different Levels of Controllability

    Jin, Y., Tintarev, N. & Verbert, K., 2018, RecSys '18 : Proceedings of the 12th ACM Conference on Recommender Systems. New York, NY: Association for Computer Machinery, p. 13-21 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  9. Explaining Credibility in News Articles using Cross-Referencing

    Bountouridis, D., Marrero Llinares, M., Tintarev, N. & Hauff, C., 2018, SIGIR workshop on ExplainAble Recommendation and Search (EARS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  10. Explanations for Groups

    Felfernig, A., Tintarev, N., Tran, T. N. T. & Stettinger, M., 2018, Group Recommender Systems. Cham: Springer, p. 105-126 22 p. (SpringerBriefs in Electrical and Computer Engineering).

    Research output: Chapter in Book/Report/Conference proceedingChapterScientific

  11. From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)

    Ferro, N., Fuhr, N., Grefenstette, G., Konstan, J. A., Castells, P., Daly, E. M., Declerck, T., Ekstrand, M. D., Geyer, W., Gonzalo, J., Kuflik, T., Lindén, K., Magnini, B., Nie, J-Y., Perego, R., Shapira, B., Soboroff, I., Tintarev, N., Verspoor, K., Willemsen, M. C. & 2 othersZobel, J. & al., N. F. E. (ed.), 2018, In : Dagstuhl Manifestos. 7, 1, p. 96-139 44 p.

    Research output: Contribution to journalArticleScientificpeer-review

  12. Generating Consensus Explanations for Group Recommendations: An exploratory study

    Najafian, S. & Tintarev, N., 2018, UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. New York, NY: Association for Computer Machinery, p. 245-250 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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