Published on Thursday, October 9, 2014 and tagged with research and recsys.
There’s a lot of research on recommender systems. There’s a lot of
other research that, while not directly mentioning recommenders, is very
relevant, including research from decades ago.
A few of my favorite old papers that I think recommender systems
researchers would do well to read (and perhaps cite):
Back to Bentham?
Explorations of experienced utility (Kahneman et al., 1997) — how
people experience and remember pain and pleasure. Strong implications
for what ratings mean and what kind of utility our recommenders should
optimize for.
A
searching procedure for information retrieval (Goffman, 1964) — this
early IR paper has the crucial insight that the relevance of an item in
a search search result list (or recommendation list) is not independent
of the items that appear before or after it. Rather, an item may be less
relevant if it is (partially) redundant with a previous item.
Published on Wednesday, October 8, 2014 and tagged with research and recsys.
In my research, I am trying to understand how different recommender
algorithms behave in different situations. We’ve known for a while that
‘different recommenders are different’1, to
paraphrase Sean McNee. However, we lack thorough data on how
they are different in a variety of contexts. Our RecSys 2014 paper, User Perception of Differences in Recommender
Algorithms (by myself, Max Harper, Martijn Willemsen, and Joseph
Konstan), reports on an experiment that we ran to collect some of this
data.
I have done some work on this subject in offline contexts already; my
When Recommenders Fail paper
looked at contexts in which different algorithms make different
mistakes. LensKit makes it easy to test
many different algorithms in the same experimental setup and context.
This experiment brings my research goals back into the user experiment
realm: directly measuring the ways in which users experience the output
of different algorithms as being different.
Published on Sunday, October 5, 2014 and tagged with food and recip.
Updated on Sunday, November 1, 2015.
I love biscuits and gravy. Their traditional form doesn’t work for
our family, however, so over the last months I’ve adapted and refined a
recipe for vegan biscuits and gravy that is now our standard Sunday
morning breakfast.
This recipe makes enough biscuits for 4 and gravy for 2.
Rumors
have been afloat that Twitter may be making a significant change to
its service: moving away from the reverse-chronological timeline in
favor of an algorithmically tuned news feed. And Zeynep
Tufekci’s critique of this prospect made the rounds, in waves,
through my Twitter stream.
I must confess, my initial reading of Tufekci’s article (as a
recommender systems researcher and developer) was somewhat knee-jerk. I
latched on to this statement:
An algorithm can perhaps surface guaranteed content, but it cannot
surface unexpected, diverse and sometimes weird content exactly because
of how algorithms work: they know what they already know.
This statement strikes me as overreaching in its claims. ‘Cannot’ is
a strong claim to make with a high evidentiary bar, and I think we just
don’t know enough about the capabilities and limits of algorithms to
capture user interest in order to say what they cannot do.
Published on Saturday, August 9, 2014 and tagged with recipe and vegetables.
Inspired by a delicious zucchini ratatouille I had at Root Cellar Cafe, I ordered a
julienne
peeler and bought some vegetables from the farmer’s market tried to
make something like it. There is still a bit of refining to do, but it
worked pretty well:
2 zucchini
1 summer squash
A small-medium tomato
1 large baby bella mushroom
Marinara sauce (I used HEB’s)
I cubed the summer squash and blanched it, then heated the marinara
sauce with the squash, the mushroom, and the tomato (both cut up).
