We all know that automated personal assistants can find you the closest gas station or sushi spot. But can that same technology be programmed to find the best ten articles for a student’s assignment? The capability is unclear, as is the advisability of the task. But what is clear is that voice-driven technology as well as arbitrary algorithms are changing the ways in which users may be driving or directed in their information tasks. Taxonomies, controlled vocabularies, and similar tools have been used for decades in delivering effective online search. But now, with Alexa and Siri potentially being in the room, shouldn’t libraries and vendors be talking about what’s operating under the hood?
Confirmed speakers for this session include: Jim Hahn, University of Illinois; Kelly Dagan, Amherst College; Chad Mairn, St. Petersburg College.
Student perspectives on personalized account-based recommender systems in libraries
A personalized account-based recommender was developed in the University of Illinois Library's mobile app interface. The recommender system (RS) was derived from data mining topic clusters of items that are checked out together. Using the library mobile RS as a prompt to understand student preferences for personalized account-based RS, structured interviews were undertaken and analyzed thematically to determine RS features and functionality desired. In the interviews, students described their perceptions of RS, together with features and functionality desired. Students indicated that they desired data stewardship and sharing levels, which provided valuable input into matters of system transparency pertaining to recommendations derived algorithmically. An unexpected finding from students was growing unease with aspects of surveillance capitalism. Academic library recommenders can distinguish themselves from commercial recommenders in several ways, including increased transparency beyond what is available in commercial systems, and by attending to the level of student privacy, and data retention a system design issue.
Alexa, get me the articles: user experience and voice interfaces in academia
Our users in academia are influenced by larger interface trends; what becomes common practice shapes user expectations. With the rise of voice assistants and other voice-controlled interfaces in our daily lives, user hopes and expectations are potentially shifting when it comes to the academic experience. I’ll discuss some of the overall themes driving how users engage with voice-controlled and discovery environments and associated user hopes for what voice-controlled interfaces could achieve.
Introducing How to Build a Personal Voice Assistant
On many levels, we are starting to see our society transformed because of artificial intelligence. One example is voice-controlled interfaces, sometimes referred to as personal voice assistants or digital assistants, where computers and humans are interacting in a variety of ways to accomplish certain tasks. In fact, experts reveal that by 2020 there will be 50 billion connected devices interacting with and reporting data in real-time via the “Internet of Things” network. In this webinar, learn the ins-and-outs of how a Google Voice AIY kit was assembled to use artificial intelligence (machine learning, natural language processing etc.) to power a personal voice assistant via Google’s Cloud Speech-to-Text and other services.
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