A research project by Andrew Moore (MLIS '16)

1. Andrew Moore- School of Information Management.
Viewing Types
- Viewing Type-Gauging
Interest
- Viewing Type- Familiar
Content
Finding Aids
-Finding Aid: Synopsis/Description
- Finding Aid: Serendipity
- Finding Aid: Visual Elements
- Finding Aid: Positive Review, Non-Personal
- Finding Aid: Positive Review, Personal
- Finding Aid: Negative Review, Non-Personal
- Finding Aid: Negative Review, Personal
- Finding Aid: Common Values/Interests
- Finding Aid: Thematic/Genre
- Finding Aid: Common Talent
- Finding Aid: Feeling/Emotional Context
External Factor Effects
- External Factors: Positive
- External Factors: Negative
- User Time Commitment
User Experience
- Negative Indicator: Personal Recommendation
- Positive Indicator: Personal Recommendation
- Negative Experience with Netflix
- Positive Experience with Netflix
- Recommender System: Positive
- Recommender System: Negative
Categories & Codes
Future Investigation:
- Delving deeper into viewing types and external factors.
- Wider Array of interview participants.
- Longer, more robust questionnaire
- Focus Groups, to allow for additional depth of information.
- Deeper investigation into users experiences with trust/belief.
Points of Interest:
- No code is strongly correlated with satisfaction & recommender
systems.
- Top three types of Finding Aids:
i. Finding Aid: Common Thematic/Genre
ii. Finding Aid: Common Talent
iii. Finding Aid: Positive Review, Personal
- The element of trust/belief is prevalent. Interviewees were more
willing to accept recommendations if they felt they could trust the
recommendation came without ulterior motives.
- Most participants had differing ideas about how Netflix creates its
recommendations for them.
The Question:
- What do Users think of Netflix recommender
systems? Could research into their methods of
search and opinions on the current system of
recommendations provide new insights?
- Current Scholarship discusses Recommender
Systems primarily from Algorithmic/Systems-
based standpoint
Methods/Investigation:
- 8 semi-structured interviews were planned and
conducted within the School of Information
Management.
- Interviews asked participants 6 questions.
- Interviewees discussed interactions with
recommender systems, and criteria they use to
determine value in recommendations.
- A Coding Schema of 22 Codes in 4 separate
categories was applied.
Results:
- Participants discussed opinions on
recommender systems, the nature of serendipity
in algorithmic recommendations and the
makings of a good/bad recommendation.
- Additional discussion took place on the nature
of Netflix recommendations and how participants
make content selection decisions
References:
Adomavicius, G., Tuzhilin, A. (2005). Towards the next generation of recommender systems: A
survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering. 17(6),
734-749. DOI: 10.1109/TKDE.2005.99
de Gemmis, M., Lops, P., Semeraro, G., Musto, C. (2015). An investigation on the serendipity problem in
recommender systems. Information Processing and Management, 51. 695-717. DOI:
http://dx.doi.org/10.1016/j.ipm.2015.06.008
Konstan, J.A., Reidl, J. (2012). Recommender systems: from algorithms to user experience. User
Modelling and User-Adapted Interaction 22(1). 101-123. DOI: 10.1007/s11257-011-9112-x
Núñez-Valdéz, E.R., et. al. (2012). Implicit feedback techniques on recommender systems applied to
electronic books. Computers in Human Behavior 28(2012), 1186-1193. DOI: 10.1016/j.chb.2012.02.001
Ortega, F., Bobadilla, J., Gutierrez, A. (2013). Incorporating group recommendations to recommender
systems: Alternatives and performance. Information Processing and Management, 49, 895-901. Retrieved from:
http://dx.doi.org/10.1016/j.ipm.2013.02.003
Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K., (2012). A literature review and classification of recommender
systems research. Expert Systems with Applications 29(2012) 10059-10072. DOI: 10.1016/j.eswa.2012.02.038
Perugini, S., Goncalves, M.A., Fox, E.A. (2003). Recommender systems research: A connection-centric
survey. Journal of Intelligent Information Systems, 23(2). 107-143. Retrieved from:
http://link.springer.com/article/10.1023%2FB%3AJIIS.0000039532.05533.99
Victor, P., Cornelis, C., De Cock, M., Pinheiro da Silva, P. (2009). Gradual trust and distrust in
recommender systems. Fuzzy Sets and Systems, 160(2009), 1367-1382. DOI:
10.1016/j.fss.2008.11.014
”If I do take a recommendation seriously from
somebody, I…take into consideration who the person
is giving the recommendation is, and what I know of
their tastes”.
“In a setting like Netflix..I’m more looking to see the
randomized things that show up on the screen”.
“If I’m tired, or burnt out on schoolwork,
then I watch something I’ve watched a
million times before, so I don’t have to
concentrate on it”
“I do not like superhero movies; I’ve never
watched one in my life, let alone on Netflix. But
I’ve been emailed by them at least a couple
times, just.. “Don’t forget! The Avengers is on
Netflix”.
“But I definitely do
look at the front
picture, whatever it is”
“Making a Murderer is not
related to Unbreakable
Kimmy Schmidt”
“I’m..more interested in reading another
book that made me feel the same way,
or had the same level of plot twist, or had
the same depth of characters, and all
these kind of things”.
“For me it’s more word of mouth
than it is ratings. I feel like the
rating system doesn’t usually
reflect my interests”.