Jim Hahn Associate Professor

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Presentation transcript:

A personalized account-based recommender within the university library mobile app Jim Hahn Associate Professor Orientation Services & Environments Librarian jimhahn@Illinois.edu

Mobile Account-Based Recommendations Discovery systems in libraries hold vast stores of user data that have not been processed with machine learning and data mining for account recommendation purposes. For the account-based recommender: a data stream of subject metadata clusters that are checked out together was developed. Related work at UIUC looked at incorporating circulation data in relevancy rankings for search algorithms (Green, Hess, Hislop, 2012).

Mobile Account-Based Recommendations Data Process Gather streams of topic clusters checked out together Use WEKA data mining for offline Machine Learning Process. In Weka, we are using the association rule called the FP-growth algorithm Store association rules in production DB for lookup within app. Also uses VuFind filters at runtime (e.g. when recommendations are requested by user).

Frequent Patterns Frequent patterns are item sets, subsequences, or substructures that appear in a data set with frequency no lese than a user-specified threshold. (J. Han et al., 2007).

Frequent Pattern Example For Example : A set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset (J. Han, et al. 2007).

Transactions --> Association Rules Month and Year Number of Anonymized Transactions Consequent Subject Association Rules July 2016 - October 2016 60,388 33,060 November 2017 - February 2017 145,304 86,000 March 2017 - June 2017 250,000 131,885 Data are reported in Hahn & McDonald, 2018.

Account-Based Recommender Interface

VuFind Privacy Policy https://sif.library.illinois.edu/prototyping/RecPrivacyPolicy.html

Other policy/functional considerations Findings from user studies (Hahn, 2019) : User interviews indicated a need for crafting recommender services in library settings with transparent functionality. requested that system designers make clear how recommendations are designed and provided. A desire among our graduate students to use recommender systems to explore interdisciplinary research domains that have otherwise not been considered.

Cited 2012. Green, Harriett, Hess, Kirk, Hislop, R., Incorporating Circulation Data in Relevancy Rankings for Search Algorithms in Library Collections. Proceedings of the 8th International Conference on E-Science, pp. 1-6. IEEE, 2012. https://doi.org/10.1109/eScience.2012.6404447 2007. Han, Jiawei, Hong Cheng, Dong Xin, Xifeng Yan. Frequent pattern mining: current status and future directions. Data Mining Knowledge Discovery, no. 15 pp. 55-86. https://doi.org/10.1007/s10618-006-0059-1

Cited 2018. Hahn, Jim, and McDonald, Courtney. Account-based Recommenders in Open Discovery Environments. Digital Library Perspectives 34.1 (2017):70-76 http://hdl.handle.net/2142/98916 2019. Hahn, Jim. User Preferences on Personalized Account-based Recommender Systems. Proceedings of the ACRL 2019 conference. http://hdl.handle.net/2142/102364