Library online catalogues enhanced by harvesting online reviews

After a pause to prepare for teaching and work on some journal articles for publication, I have now returned my attention to my research proposal and in particular, my research problem. I have begun the process of narrowing down my idea again, and am exploring the possibility of studying "Blogs as narrative information sources for knowledge sharing". In this blog post, I just want to share with you some of my thinking through the issues as I formulate my research problem. Lets begin with a fictional story to represent an aspect of my problem.


Joseph searches the library online catalogue on restaurants. After reviewing the results retrieved, he shakes his head. Which of these resources should he check out? Which one will be worth his time? Why doesn't the library online catalogue provide any signals to advise him about the content in these resources? Does he have to go to Google or Amazon to read reviews for each material before taking the time to retrieve or browse them?

What Joseph contemplates is not far from reality. Libraries are already implementing online catalogues that draw on summaries and reviews from other online websites to help given readers more information about catalogue entries.  In my observation, I notice that the London Public Library (in Canada) uses the Encore, a product of Innovative Interfaces Inc., which facilitates reviews from library users/readers (Encore, 2012?), and even seems to harvest reviews and ratings from Goodreads.com. This is of interest to me, because I am developing a research proposal with the idea that one of the practical implications of my research is the harvesting of blog content to enrich library catalogue entries.

According to the overview of Encore on the Website:

Encore offers a suite of applications and web services that delivers a universe of information in ways that are intuitive, relevant, and, perhaps most important, familiar to today’s internet users. Through a single search box, Encore connects users to all the trusted resources the library collects or selects. Plus, Encore gives users ways to connect with each other and participate in your library’s information landscape.

How? Encore elegantly presents all manner of discovery tools, including faceted search results, Tag Cloud, Did You Mean…?, Popular Choices, Recently Added suggestions, and RightResult™ relevance ranking. It integrates federated search, as well as enriched content—like first chapters—and harvested data, and facilitates community participation with user tagging and community reviews.
 

Consequently, it appears that our online catalogues and discovery systems are already making use of online reviews generated by ordinary users to enrich the information in the OPACs.

Another Canadian library, the Toronto Public Library also uses a similar service called Syndetic Solutions™ from Bowker. According to its website, Syndetic Solutions™ from Bowker “is the premier source of specialized, high-quality bibliographic data designed to enhance library online catalogs”. It also offers Syndetics Classic™  which reportedly provides:

a wealth of descriptive information and cover images relating to videos, DVDs, CDs, audio books, and all types of books—from young adult chapter books to conference proceedings. Various elements of content are added weekly for over hundreds of thousands of new titles each year. Syndetics Solutions™ strives to provide a wide variety of the most useful and highest quality information available, much of which can not be found on online booksellers' catalogs and not available from any other source. New options are constantly being added to the service .

Hence Syndetics Solutions™ seeks to enhance the online public access catalogues of libraries through displaying descriptive data about the resources within the library's collection that can signal to readers the content within particular resources. Among the the descriptive data are summaries and annotations, tables of contents, author notes, book reviews, topical headings, images of book covers, and actual excerpts from within the books (Bowker, 2011). Of interest here are the reviews, of which Bowker (2011) reports that Syndetic Solutions product, Syndetics Classic, offers more than 2.8 million reviews as part of its enrichment elements. According to the its FAQ page,  Syndetics Solutions harvests its reviews from the following publications:

Library Journal - coverage beginning with 1985

School Library Journal - coverage beginning with 1985

Publishers Weekly - coverage beginning with 1985

Criticas - coverage beginning with 1999

Booklist - coverage beginning with 1988

Choice - coverage beginning with 1988

Horn Book - coverage beginning with 1985

Kirkus Reviews - coverage beginning with 1983

New York Times – coverage begins with 2007

Doody’s Reviews – coverage beginning with 1993

Quill and Quire – coverage beginning with 1996

Voya (Voice of Youth Advocates) – coverage beginning with 1993

As such, Syndetics, unlike Encore, does not harvests its reviews from any ordinary person online, but rather from selected and "trusted" publishers.

My viewpoint on this matter is that while "authoritative" and "trusted" reviews by so-called "experts" are useful, we cannot ignore the ordinary or lay person's own review. According to a Technorati (2013) report, "blogs rank among the top five “most trustworthy” sources" that consumers use to make purchasing decisions (p. 4). Further, a study has shown that a good portion of consumers (approximately 70%) trust in and value online reviews similar to personal recommendations (Anderson, 2010).  In addition, it has been found by Johnson et al. (2008), that blogs have been deemed as highly credible sources of information for those who use them (albeit biased sources). As such, the data shows that in the online environment, online users desire authenticity, candid remarks, the biases and personal viewpoints expressed in online reviews in general and in particular, those views expressed on blogs. Which is why, my current research in validating blogs as information sources and narrative artifacts for knowledge sharing is important.



References:

Anderson, M. (2010, Nov 29). Local Consumer Review Survey 2010 – Part 1. BrightLocal Retrieved from: http://www.brightlocal.com/2010/11/29/local-consumer-review-survey-2010-part-1/

Bowker (2011). Syndetics classic: Enrichment elements. Retrieved from http://www.bowker.com/en-US/products/syndetics/classic/enrichment_elements/book_reviews.html


Encore (2009, May 22). Twelve libraries launch Encore 3.0: Libraries implement ratings, reviews, new discovery features, and more. Retrieved from http://encoreforlibraries.com/2009/05/26/twelve-libraries-launch-encore-30/

Encore (2012?). London Public Library (Canada) patrons embrace social participation. Retrieved from http://encoreforlibraries.com/2012/08/20/lp/

Johnson, T. J., Kaye, B. K., Bichard, S. L., & Wong, W. J. (2008). Every Blog Has Its Day: Politically-interested Internet Users' Perceptions of Blog Credibility. Journal of Computer-Mediated Communication, 13(1), 100--122. Retrieved from ttp://jcmc.indiana.edu/vol13/issue1/johnson.html

Technorati. (2013). TechnoratiMedia. 2013 Digital influence report. Retrieved from http://technoratimedia.com/wp-content/uploads/2013/02/tm2013DIR.pdf

 

The future of readers' advisory: Will technologies usurp this role of libraries?

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.


Reference:


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.

Proto-type of a story-based information retrieval system for libraries

Sometimes, it is easier for me to communicate my thoughts via a slide presentation, rather than textual information. This blog post is one of those post in which I find it easier to communicate my ideas in a slide format drawing on storytelling concepts to express an online information system that I am thinking of which could be applied to the new library online public access catalogues. In this slide, the inspiration for my ideas or prototype comes from two authors: Schank (1982) and Laurel (1993) (see their references below).

References/Inspiration:

Laurel, B. (1993). Computers as theatre. Reading, Mass.: Addison-Wesley Pub. Co.

Schank, R. C. (1982). Dynamic memory :A theory of reminding and learning in computers and people. Cambridge Cambridgeshire; New York: Cambridge University Press.