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Evaluation of Recommender Algorithms for an Internet Information Broker based on Simple Association Rules and on the Repeat-Buying Theory WEBKDD 2002 Edmonton, Alberta, Canada. Michael Hahsler Andreas Geyer-Schulz. Introduction. Research questions

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michael hahsler andreas geyer schulz

Evaluation of Recommender Algorithms for an Internet Information Broker based on Simple Association Rules and on theRepeat-Buying TheoryWEBKDD 2002Edmonton, Alberta, Canada

Michael Hahsler

Andreas Geyer-Schulz

introduction
Introduction
  • Research questions
    • Are frequent itemsets (association rules) useful to build recommender systems for information brokers?
    • How does a recommender based on association rules perform compared to a recommender based on the repeat-buying theory known from marketing research?
  • Framework for evaluation
  • Item-to-item correlation type recommender
  • Association rules / Repeat-buying theory
  • Experimental setup
  • Results and conclusion
framework for evaluation
Framework for Evaluation

(Fayyad et al. 1996)

added

performance measures for recommender algorithms
Performance Measures for Recommender Algorithms
  • From Machine Learning
    • Accuracy
    • Coverage
    • MAE
  • From Information Retrieval
    • Precision / recall
    • F-measure (F1) / E-measure
  • Other measures
    • Receiver Operating Characteristic (ROC)
    • Mobasher\'s R
item to item correlation type of recommender
Item-to-item Correlation Type of Recommender
  • Recommendations can be based on many sources of information (e.g. Ansari et al 2000):
    • Expert evaluations
    • Expressed preferences
    • Individual characteristics
    • Item characteristics (content based)
    • Expressed preferences of other people (collaborative)
  • We analyze non-personalized transactions where items are used together.

explicit, observations

association rules agrawal et al 1993
Association Rules (Agrawal et al, 1993)
  • Support-Confidence Framework
  • Model assumptions: none
  • We use only Rules with 1 item in the antecedent for recommendations
  • Criticism: confidence ignores the frequency of the consequent. Other measures of interestingness are
    • Lift - symmetric measure of co-occurrence (deviation from independence)
    • Conviction - deviation of the implication from independence
repeat buying theory ehrenberg 1988
Repeat-buying Theory (Ehrenberg 1988)
  • Model assumptions:
    • Stationary (in the analyzed time period)
    • "Purchases" of items follow independent Poisson processes
    • Distribution of the parameters follows a Gamma-distribution
    • Used for panel data for consumer products (scanner data)
  • LSD (NBD) gives the probability that an item (combination of items) is "purchased" 1,2,...,r times by pure chance.
repeat buying theory recommender
Repeat-buying Theory (Recommender)
  • Users "purchase" some items more often together than predicted
  • Filter outliers as potential recommendations

Outliers

Co-occurence of

2 Web pages

from out Dept.\'s

home page in the

Web Server\'s log

experimental setup info broker
Experimental Setup (Info. Broker)
  • Educational Internet Information Broker for lecture notes, research material, enrollment information,...
experimental setup data set
Experimental Setup (Data Set)
  • Available Data:

6 months of transaction data (January - June 2002)

Transactions: 25522

Items: 3774

Co-occurrences: 114128

  • Data Set:

co-occurrence lists for 300 items randomly selected

9 users from the target group evaluated 1661 co-occurrences

561 "recommend" - 1100 "don\'t recommend"

results support for association rules
Results (Support for Association Rules)
  • Low support necessary for reasonable recall
results lift conviction
Results (Lift / Conviction)
  • Lift / conviction don\'t perform sig. better than confidence.
results precision accuracy
Results (Precision, Accuracy)
  • Perform better than a random choice
  • Support-Confidence sensitive to misspecification (conf)
results mae
Results (MAE)
  • Support-Confidence sensitive to misspecification (conf)
  • Repeat-buying seems more robust
results parameter of the repeat buying recommender
Results (Parameter of the Repeat-buying Recommender)
  • Expected error II rate is below the avg. rate (exception thresholds 0, 0.1, 0.2)
conclusion
Conclusion
  • Frequent itemsets represent useful recommendationsAccuracy >70%, Precision 60-90%
  • Both algorithms perform very similar with adequate parameters.
  • Repeat-buying algorithm seems more robust to misspecification
  • Model assumptions of the repeat-buying theory need to be checked.
  • Datasets from other applications are needed to confirm findings
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