the effect of correlation coefficients on communities of recommenders
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the effect of correlation coefficients oncommunities of recommenders
neal lathia, stephen hailes, licia capra
how do we model recommender systems?
a) machine learning
user ratings recommendationsmodel-based collaborative filtering
b) collaborative filtering
user ratings matrixrecommendationsmemory-based
collaborative filtering
how do we think about this?
collaborative filtering: a network of cooperating
usersexchanging opinions
nodes = userslinks = weighted according to similarity
community view of therecommender system:
0.75
-0.43
0.2
0.57
(a very small example)
or, put another way:
good
bad
good
good
(the relationships in the community)
the similarity values depend on how you derive
similarity
pearson:-0.50
weighted-pearson:-0.05
vector:0.76
= no agreement
ratings:[2,3,1,5,3]
ratings:[4,1,3,2,3]
pearson:bad
weighted-pearson:no similarity
vector:good
= no agreement
ratings:[2,3,1,5,3]
ratings:[4,1,3,2,3]
so what is the best way to build the recommender
system network?
like this?
good
bad
good
good
or like this?
bad
good
bad
good
or like this?
nosimilarity
good
good
bad
each way will change the distribution of values over
the network:
(let’s look at it on the movielens dataset)
pearson distribution:
other distributions:
a) accuracy: how well we can make predictions about
unknown items
what if we did this?
(random number)
(expect terrible results)
the results are far from terrible!
b) coverage: what proportion of items we can
find useful information about (to make predictions)
before:look for information from the top-k neighbours
(expect top-k to do quite well)
what if we did this?look for information from anyone who has rated the item
the results are terrible
(best coverage when all of community used)
why is all of this happening?
a) our error measures are not good enough?
N
rpMAE
iaia ,,
a) is there something wrong with the dataset?
…it does have the long-tail
c) is user-similarity not strong enough to
capture the best recommender relationships
in the network?
future: trust-based recommender systems
(neal’s phd)
the effect of correlation coefficients oncommunities of recommenders
neal lathia, stephen hailes, licia capra
all the details in the paper…
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