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Analysis of Recommendation
Algorithms for E-Commerce
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl
{sarwar, karypis, konstan, riedlg}@cs.umn.edu
GroupLens Research Group / Army HPC Research Center
Department of Computer Science and Engineering
University of Minnesota
Minneapolis, MN 55455
ESOE R99525045 郭羿呈 1
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Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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3.
Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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7.
Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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8.
Abstract
• In this paper, Author investigate several
techniques for the purpose of producing useful
recommendations to customer.
• Author apply a collection of algorithms
– Data mining
– Nearest-neighbor collaborative filtering
– Dimensionality reduction
• Applied to two different data sets
– The web-purchasing transaction of a large E-
commerce
– MovieLens movie recommendation site.
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9.
Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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10.
Introduction
• There remain important research questions in
overcoming two fundamental challenges for
collaborative filtering
– To improve the scalability
– To improve the quality of the recommendations
• In some ways these two challenges are in
conflict.
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Problem Statement
• Author research these two challenges together.
• There has been little work on experimental
validation of recommender systems against a set
of real-world dataset. More experimental
validation is needed against real-world datasets
and it is important.
• Author seek to investigate how the quality of their
recommendations compares to other algorithms
under different practical circumstances.
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12.
The focus of this paper is two-fold
• Author provide a systematic experimental
evaluation of different techniques.
• Author present new algorithms that are
particularly suited for sparse data sets.
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Contributions
• An analysis of the effectiveness of recommender
systems on actual customer data from an e-commerce
site.
• A comparison of the performance of several different
recommender algorithms, including original
collaborative filtering algorithms, algorithms based
on dimensionality reduction, and classical data
mining algorithms.
• A new approach to forming recommendations that has
online efficiency advantage versus, and that also has
quality advantages in the presence of very sparse
dataset.
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14.
Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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Recommender systems
• They apply data analysis techniques to the
problem of helping customers find which
products they would like to purchase at E-
Commerce sites by producing a list of top
recommended products for a given customer.
• In this section we discuss some traditional data
mining techniques, particularly, we discuss the
association rule technique and how this
technique can be effectively utilized to produce
top-n recommendation generation.
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16.
Traditional Data Mining: Association
Rules
• One of the most commonly used data mining
techniques for E-commerce is finding
association rules between a set of co-
purchased products.
• More formally,
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The quality of association rules is
commonly evaluated by looking at
their support and confidence.
• Support
• Confidence
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Association rules can be used to
develop top-N recommender systems
in the following way.
1. We then use an association rule discovery algorithm to
find all the rules that satisfy given minimum support
and minimum confidence constraints.
2. We find all the rules that are supported by the
customer. Let P be the set of unique products that are
being predicted by all these rules and have not yet
been purchased by customer u.
3. We sort these product them, so that products predicted
by rules that have a higher confidence are ranked first.
4. Finally, we select the first N highest ranked products
as the recommended set.
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Recommender Systems Based on
Collaborative Filtering
• Collaborative filtering (CF) is the most
successful recommender system technology to
date, and is used in many of the most
successful recommender systems on the Web.
• CF systems recommend products to a target
customer based on the opinions of other
customers.
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Author divide the entire process of CF-
based recommendation generation
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Representation
• In a typical CF-based recommender
system, the input data is a collection of
historical purchasing transactions of n
customers on m products. We term this m x
n representation of the input data set as
original representation.
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Alternate methods for representing the
input data
• A natural way of representing sparse data sets is
to compute a lower dimensional representation
using Latent semantic indexing (LSI).
• Essentially, this approach takes the n x m
customer-product matrix and uses a truncated
singular value decomposition to obtain a rank-k
approximation of the original matrix.
• Author will refer to this as the reduced
dimensional representation. This representation
has a number of advantages.
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Alternate representation has a number
of advantages.
1. It alleviates the sparsity problem as all the
entries in the n x k matrix are nonzero, which
means that all n customers now have their
opinions on the k meta-products.
2. The scalability problem also gets better as k <<
n, the processing time and storage requirement
both improve dramatically.
3. This reduced representation captures latent
association between customers and products in
the reduced feature space and thus can
potentially remove the synonymy problem.
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Author divide the entire process of CF-
based recommendation generation
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Two main schemes for neighborhood
formation
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Author divide the entire process of CF-
based recommendation generation
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Generation of Recommendation
• Most-frequent Item Recommendation
– looks into the neighborhood N and for each
neighbor scans through his/her purchase data and
performs a frequency count of the products.
• Association Rule-based Recommendation
– is based on the association rule-based top-N
recommendation. However, instead of using the
entire population of customers to generate
rules, this technique only considers the l neighbors
while generating the rules.
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31.
Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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Sparsity level of data sets
• For author’s experiments, author also take
another factor into consideration, sparsity level
of data sets.
• For the data matrix R This is defined as
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The first data set
• Movie data (ML)
– Author used data from our MovieLens recommender
system, MovieLens is a web-based research recommender system that
debuted in Fall 1997.
– The site now has over 35000 users who have expressed opinions on
3000+ different movies.
– We randomly selected enough user to obtain 100,000 ratings from the
database (we only considered users that had rated 20 or more movies).
– We divided the database into 80% training set and 20% test set.
– The data set was converted into a binary user-movie matrix R that had
943 rows (i.e., 943 users) and 1682 columns (i.e., 1682 movies that
were rated by at least one of the users).
– We compute the sparsity level for this data set and found it to be
0.9369.
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The second data set
• E-Commerce data (EC)
– Author use historical e-commerce purchase data
from Fingerhut Inc., a large e-commerce company.
– This data set contains purchase information of 6,502
customers on 23,554 catalog products. In total, this
data set contains 97,045 purchase records.
– As before, we divided the data set into a train set and a
test set by using the same 80%/20% train/test ratio.
– We compute the sparsity level for this data set and
found it to be 0.9994.
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Two metrics widely used in the IR
community
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Author use the standard F1 metric
• These two measures are, however, often
conflicting in nature. For instance, increasing the
number N tends to increase recall but decrease
precision.
• The fact that both are critical for the quality
judgment leads us to use a combination of the
two.
• In particular, we use the standard F1 metric that
gives equal weight to them both and is computed
as
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Experimental Results
• Our main goal is to explore the possibilities of
combining different subtasks to formulate an
efficient recommendation algorithm.
• In all the CF-based experiments the proximity
between customers was measured by using cosine
metric. The cosine metric was selected because it
is applicable both in original and lower
dimensional representations.
• Finally, in all author’s experiments author fixed
the number of recommendations at 10 (i.e., top-
10).
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Experiments with number of
dimension
P.S. There is no direct analytical method to determine the value of the optimal number of dimensions so
the optimal value has to be experimentally evaluated. For the rest of the experiments we fixed the number
of dimensions to 20 for the ML and 300 for the EC data set.
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Summarizes in both high and low
dimensional settings
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42.
Outline
• Why this paper?
• Abstract
• Introduction
– Problem Statement
– Contributions
• Recommender systems
– Traditional Data Mining: Association Rules
– Recommender Systems Based on Collaborative Filtering
• Experimental evaluation
– Data sets
– Evaluation Metrics
– Experimental Results
• Conclusion
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Conclusion
• In this paper we presented and experimentally evaluate
various algorithmic choices for CF-based recommender
systems.
• Author’s results show that dimensionality reduction
techniques hold the promise of allowing CF-based
algorithms to scale to large data sets and at the same
time produce high-quality recommendations.
• Future work is required to understand exactly why low
dimensional representation works well for some
recommender applications, and less well for others.
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