So I am now back from my social media break and ready to blog again, especially about the interesting things I am reading and discovering on the Web. One such reading that I found interesting this week is the article by Grant (2013) on Trapit (Siri's relative) and an iPad app that uses natural language processing to recommend content to read. In short, Trapit offers the possibility of readers' advisory, a service that has traditionally been offered by libraries.
For the uninitiated, readers' advisory is a service provided by libraries, whereby librarians, based on an understanding of the reading or information needs and preferences of their users, provide guidance or advice about resources or readings that users should consider reading next. Reitz (2012) explains that such a service is provided by:
an experienced public services librarian who specializes in the reading needs of the patrons of a public library. A readers' advisor recommends specific titles and/or authors, based on knowledge of the patron's past reading preferences, and may also compile lists of recommended titles and serve as liaison to other education agencies in the community.While traditionally, reader advisory would be a service offered by libraries, technology is rapidly usurping that role. With Internet developments, such as recommender systems provided by Amazon and Google Books, users do not need to interface with library personnel in order to get suggestions about what to read next. Technology now offers to look at what we have read in the past and our preferences, using these to predict what else may be of interest to us.
Going back to specific app called Trapit, I already see application of this software within large companies with many documents. Grant (2013) suggests that Trapit has the capability to personalize business news and internal reports, for employees to quickly access information relevant to them without searching for it.
Prior to learning about Trapit, I was of the view that artificial intelligent conversational agents (or chatbots) could also play roles as readers' advisors if programmed to ask questions and recommend books based on user responses. However, to explain this, I will need to write an entire technical paper and not just a blog post. Sufficient to say that Rubin, Chen & Thorimbert (2010) have already proposed that libraries use agent for storytelling and for even leading book discussions. Hence, agents can talk about books, though requiring initial and continual human investment in time to update and make them useful. Trapit in contrast, promises to require little or no input from staff, but only end users.
To conclude, the readers' advisory service of libraries seems to be one that will change in the near future. As more persons adopt tablet and other computing devices, our libraries will perhaps play a role in training users to use and customise the technologies for content recommendations, rather than actually engage in face-to-face dialogue for making recommendations of titles for reading. In fact, such services could also be built automatically in our future (if not present) online catalogues and other electronic systems.
Grant, R. (2013, April 3). Content-recommendation app Trapit grows up, enters formidable world of publishing. VentureBeat. Retrieved from http://venturebeat.com/2013/04/03/siris-little-brother-trapit-grows-up-enters-formidable-world-of-publishing/
Reitz, J. M. (2012). ODLIS: Online dictionary for library and information science. Retrieved from http://www.abc-clio.com/ODLIS/searchODLIS.aspx
Rubin, V. L., Chen, Y. & Thorimbert, L. M. (2010). Artificially intelligent conversational agents in libraries Library Hi Tech, 28(4), 496-522.