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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 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

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  1. 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

  2. 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

  3. Framework for Evaluation (Fayyad et al. 1996) added

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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

  9. Experimental Setup (Info. Broker) • Educational Internet Information Broker for lecture notes, research material, enrollment information,...

  10. 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"

  11. Experimental Setup (Evaluated Data)

  12. Results (Support for Association Rules) • Low support necessary for reasonable recall

  13. Results (Lift / Conviction) • Lift / conviction don't perform sig. better than confidence.

  14. Results (Precision, Accuracy) • Perform better than a random choice • Support-Confidence sensitive to misspecification (conf)

  15. Results (MAE) • Support-Confidence sensitive to misspecification (conf) • Repeat-buying seems more robust

  16. Results (Parameter of the Repeat-buying Recommender) • Expected error II rate is below the avg. rate (exception thresholds 0, 0.1, 0.2)

  17. 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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