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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.