Recommender Applications

Some of my recommender and information retrieval systems research has been application-focused.

Recommendation & Retrieval for Teachers

Two students reading on a laptop with their teacher

The LITERATE project seeks to help teachers more effectively locate current and authentic informational texts for use in their classrooms. For more details, see the project page.


Michael D. Ekstrand, Katherine Landau Wright, and Maria Soledad Pera. 2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (June 15th, 2020), 725–744. DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 16 times. Cited 11 times.


Katherine Landau Wright, David McNeill, Michael D. Ekstrand, and Maria Soledad Pera. 2019. Supplementing Classroom Texts with Online Resources. At 2019 American Educational Research Association Conference. Cited 17 times.


Katherine Landau Wright, Michael D. Ekstrand, and Maria Soledad Pera. 2018. Supplementing Classroom Texts with Online Resources. At 2018 Annual Meeting of the Northwest Rocky Mountain Educational Research Association.


Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18), at RecSys 2018. Cited 6 times. Cited 3 times.


Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec ’18), at IDC 2018. DOI 10.18122/cs_facpubs/140/boisestate. Cited 3 times. Cited 6 times.

Searching for Research Papers

My first recommender systems research project was a research paper recommender. The key idea of this project was to blend graph ranking algorithms (such as HITS, SALSA, and PageRank) with a collaborative or content-based filter; our goal was to make a recommender that was particularly good at producing reading lists for junior researchers.

We did a small user study indicating some modest improvement from the addition of the graph ranking. While our overall research method was sound and implemented the high-level research pipeline that I recommend, the user study we conducted in this should not be used as the starting point for new studies. Much better user study methods are now available.


Michael D. Ekstrand, Praveen Kannan, James A. Stempter, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys ’10). ACM, pp. 159–166. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 123 times. Cited 101 times.