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

2. 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).

3. 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).

4. 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).

5. 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).

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

7. Account-Based Recommender Interface

8. VuFind Privacy Policy

9. 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.

10. Cited
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 IEEE,
2007. Han, Jiawei, Hong Cheng, Dong Xin, Xifeng Yan. Frequent pattern mining: current status and future directions. Data Mining Knowledge Discovery, no. 15 pp

11. Cited
2018. Hahn, Jim, and McDonald, Courtney. Account-based Recommenders in Open Discovery Environments. Digital Library Perspectives 34.1 (2017):
2019. Hahn, Jim. User Preferences on Personalized Account-based Recommender Systems. Proceedings of the ACRL 2019 conference.