recsplorer: recommendation algorithms based on precedence mining acm sigmod conference 2010 1
TRANSCRIPT
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Recsplorer: Recommendation Algorithms Based on Precedence Mining
ACM SIGMOD Conference 2010
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Outline
Introduction
Approach
Algorithms
Popularity Algorithm
Single Item Max-Confidence Algorithm
Joint Probabilities Algorithm Approximation
Joint Probabilities Support Variant
Joint Probabilities Hybrid Variant
Joint Probabilities Hybrid Reranked Variant
Evaluation
Conclusions
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Introduction
Recommender systems provide advice on products, movies…,and so on.
collaborative filtering (CF)
without regard to order
few items are rated by few users
precedence mining
based on temporal
does not suffer from the sparsity of ratings problem
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Approach
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Approach_Collaborative Filtering
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Approach_Precedence relationships
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definition
f(x):
the number of transcripts in T that contain x.
g(x; y):
the number of transcripts in T in which x precedes course y.
f(a)=2,f(b)=2
g(a,d)=2,g(e,f)=2,g(g,h)=2
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definition
# of transcripts containing a and x / # of transcripts containing a
But our user's transcript does not have x before a.(ignore #5)
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definition
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Top-k Recommendation Problem
Given a set T of transcripts over D for n users, the extra transcript T of a target user, and a desired number of recommendations k,our goal is to
Assign a score score(x) to every course ,
Using the score function, select the top k courses to recommend to the target user.
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RECOMMENDATION ALGORITHMS
Popularity Algorithm
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example
D = {a , b , c , d}
n = 50 students=6/50=0.12
=4/50=0.08
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RECOMMENDATION ALGORITHMS
Single Item Max-Confidence Algorithm
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example
D = {a , b , c , d} , T={a , b}
n = 50 students
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RECOMMENDATION ALGORITHMS
Joint Probabilities Algorithm Approximation
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example
D = {a , b , c , d} , T={a , b}
n = 50 students
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RECOMMENDATION ALGORITHMS
Consider a course x that has appeared in 1000 transcripts,
while y appeared in 10 transcripts.
Assume the student has not taken neither x nor y.
If there are 20 course (small) (big)
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RECOMMENDATION ALGORITHMS
Joint Probabilities Support Variant
where for any not-taken course x
if f(x)<θ(for some threshold)
then assign score(x) = 0
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RECOMMENDATION ALGORITHMS
Assume a set of courses all appearing in the transcript T .
Consider course x that we wish to recommend to a user.
It may be the case (especially when the data is sparse) that x is strongly suggested by courses
but we may still not recommend x because of .
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RECOMMENDATION ALGORITHMS
Joint Probabilities Hybrid Variant
Step 1:
assign a score of 0 to not-taken courses where
Step 2:
assign a score to a remaining course x we proceed as follows.
We set top-I(T) to be the top I courses from T
ranked by
Step 3:
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RECOMMENDATION ALGORITHMS
Reranked Hybrid Variant
Step 1:
Take the set O of courses recommended by the Joint Probabilities Hybrid Variant
Step 2:
pick the best m courses recommended
Step 3:
for each courses x in the remaining courses,
Assign
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RECOMMENDATION ALGORITHMS
Step 4:
Order this top-m set in inverse order of f(x).
i.e. if x and y are in the set, x is ranked higher
than y if f(x) < f(y)
Step 5:
for the first coure in top-m set, assign score= for the second coure in top-m set, assign score=
for the final coure in top-m set, assign score= ,
and so on
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EVALUATION
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EVALUATION
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CONCLUSIONS
The Single Item Max Confidence approach has the highest precision when we have little information about the student.
Joint Prob. Hybrid works best with more information at hand.
we found that algorithms beat popularity-based recommendations and collaborative filtering.