Users are notified of the account based recommender policy from a library webpage; they are notified that the policy is a developed to support machine learning processes for service delivery. Library faculty and staff will receive updates to the policy through the library listserv or Faculty meeting updates. If the policy is updated, users will be notified through their accounts that a new library recommendation policy is available. The data collected by this experimental system will be stored on the University Library’s secure servers. The data will not include any information that identifies an individual. The only people who will have access to the data include researchers associated with the project and Library IT staff as needed for maintenance. The data mining of VuFind account actions and Voyager checkout clusters will be utilized for service improvements and Library service excellence within VuFind accounts and services that utilize or build on VuFind accounts like Minrva for Android, Minrva for iOS, and the VuFind login from the Library webpage(s). Recommendation data will be used to improve email communications from the Undergraduate Library where technology items will be recommended based on previously checked out equipment by the user.
A user can contact email@example.com to request information about their IP address from the technical support team. Data about user names and library accounts are not recorded or available. Only a user’s IP address may be retained. These data points can be removed but not revised since they are simply server records. The data used for machine learning are stored in clusters and combined with other data points from Voyager, so individual access by a patron cannot be provided. An individual is not able to contest data accuracy or completeness with specific questions about their participation in the recommender service.
Data are stored in password protected servers. Only the technical staff that support this service has access to the data. Data are kept indefinitely for research purposes aimed at improving the app. Individual data is anonymized by way of unique identifiers in the place of a user identifiers. data are anonymized by way of unique identifiers in the place of a user identifiers. Since individual data are anonymized, individually identifiable data are not retained.