Recommender Applications
Some of my recommender and information retrieval systems research has been application-focused. 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. 2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (June 2020), 725β744. AJIM 72(5) (June 2020). DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 9 times. Cited 12 times. 2019. Supplementing Classroom Texts with Online Resources. At 2019 American Educational Research Association Conference. 2018. Supplementing Classroom Texts with Online Resources. At 2018 Annual Meeting of the Northwest Rocky Mountain Educational Research Association. 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 β18), at RecSys 2018. Proc. ComplexRec β18. Cited 4 times. Cited 7 times. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec β18), at IDC 2018. Proc. KidRec β18. DOI 10.18122/cs_facpubs/140/boisestate. Cited 7 times. Cited 6 times. While interning at Autodesk Research, I prototyped and studied a context-aware help search tool that would monitor the userβs interactions with a software program and use the collected data to enhance searches for help resources on the web. Our prototype and experiment were an interesting hack. We instrumented the Inkscape vector drawing program to report user activity to a context monitor process. The user then searched for help resources using a customized browser; this browser used JavaScript injection to enhance Google search results, both by incorporating context information into the result display and by mixing the results with the results of additional searches produced by query augmentation. 2011. Searching for Software Learning Resources Using Application Context. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST β11). ACM, pp.Β 195β204. Proc. UIST β11. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 48 times. Cited 53 times. 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. 2010. Automatically Building Research Reading Lists. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys β10). ACM, pp.Β 159β166. Proc. RecSys β10. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 101 times. Cited 118 times.Recommendation & Retrieval for Teachers
Context-Aware Help Search
Searching for Research Papers