Can’t decide what book to read next? Are book recommendations from Amazon not useful? Then go to http://www.goodreads.com a free online platform that prides itself in its customized recommendation system to match you with your future favorite book.
Goodreads combined several information systems to create an online platform connecting books (and authors) to readers. Goodreads calls itself a “social cataloging” website, which more or less means it’s an online platform co-created using crowdsourcing to build an extensive catalogue and information search system combined with an intricate algorithm to base its recommendation system on.
The platform started from a mission “to help people find and share books they love… [and] to improve the process of reading and learning throughout the world,” (Goodreads.com). In order to dare to attempt such a lofty dream the founders turned to crowdsourcing, allowing people to join the community for free and upload books and journals. Users are able to make their own profiles, reading lists, start forums, and discussions, and create their own group of book recommendations; attracting over 20 million users, causing the “net-work effect” and a subsequent library of over ten million books and growing(Eisenmann, 2006).
Choice overload! What to choose!? No fear, the website prides itself in its accurate Recommendation Agent (RA) algorithm. When a user signs up the first thing they do is go through active content-filtering by selecting preferences for book genres. The second step is to review/rate 20 books which the RA uses as collaborative filtering to recommend books based on matching criteria using the opinions of “like minded people”(Xiao, et al., 2007).
One of the reasons the RA algorithm is such a success is because of its inherent design activating customers. Since most people want to put in minimum effort and find long questionnaires to be annoying, it is important that Goodreads has designed an interactive questionnaire that customizes depending on the individual respondent and their previous responses and preferences, shortening the number of questions needed to gather information and less of a burden for the user, and designed to to increase accuracy over repeated trials(Murray, et al, 2011).
The RA does not only gather information from questionnaires and book reviews, but also from recommendations and voting contests. The website has many features where you can give, ask for and view recommendations from other users, or view one of the many lists of “winning” books. The most famous feature is, “Listopia,” which is the “Best of” Lists, ex: “Best Books Ever, Best Tween Books, etc.” Goodreads has created an online community where users can vote and give book reviews deciding the “best of” lists, another sneaky way to gather consumer preferences.
Although Goodreads was criticized heavily for the initial design of the unfiltered/ uncensored review system, many reviews were found to be abrasive, containing racial slurs, sexual remarks, and profanity; others used it as a forum to attack authors. Goodreads responded by instituting an “anti-bullying policy” which “authorized the removal of abusive content, sparking a great deal of controversy amongst authors and readers”. Even successful websites like Goodreads can receive backlash if they do not create an Institutional Environment with “clear (social) norms of cooperative behavior,” because “the clearer the norms… the greater the scope for beneficial exchanges to go through,” (Carlson, et al., 1999).
- Carson, Stephen J., et al. “Understanding institutional designs within marketing value systems.” Journal of Marketing (1999): 115-130.
- Eisenmann, Thomas, Geoffrey Parker, and Marshall W. Van Alstyne. “Strategies for two-sided markets.” Harvard Business Review 84.10 (2006): 92.
- Murray, Kyle B., and Gerald Häubl. “Personalization without interrogation: towards more effective interactions between consumers and feature-based recommendation agents.” Journal of Interactive Marketing 23.2 (2009): 138-146.
- Xiao, Bo, and Izak Benbasat. “E-commerce product recommendation agents: use, characteristics, and impact.” MIS Quarterly 31.1 (2007): 137-209.