1. 1 Business System Analysis & Decision Making – Data Mining and Web Mining Zhangxi Lin ISQS 5340 Summer II 2006

2. 2 Outline Estimating the propensity to buy Online recommendation

3. 3 Estimating the Propensity to Buy

4. 4 Goals for Building Propensity-to- Buy Models Use propensity-to-buy scores for personalization and to influence dynamic Web content Test interventions that are intended to increase the probability of the user making a purchase Use propensity-to-buy models to evaluate banner ads with respect to increasing the propensity to buy at the target Web site

5. 5 Issues Related to Propensity-to- Buy Models Web log data alone is insufficient to build these models. Implementation issues and the use of dynamic inputs need to be resolved before deciding on candidate models. Binomial response modeling is required, but as an alternative, you can predict the amount spent in the current session, or you can employ a two-stage model that predicts both propensity to buy and amount spent.

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7. 7 Variable Definitions

8. 8 Variable definitions (Cont’d)

9. 9 SAS Enterprise Miner Model DatasetDataset in partitioned Modeling in a Decision tree Assess the outcome

10. 10 Decision Tree

11. 11 Lift Value

12. 12 Captured Response

13. 13 Improving a Model Add more inputs that may influence the target. Experiment with transforming inputs. Combine predictions using an average of two or more models. Oversample when the success rate is small, as it is in the propensity-to-buy example. Incorporate Bayesian concepts into the modeling process: prior distributions, profit and loss matrices (use the target profile tab/option in Enterprise Miner).

14. 14 Online Recommendation

15. 15 Statement of the Problem There are M judges and N items. Each judge j will have assigned a rating R(j,k) to item k if the judge has evaluated item k, otherwise the rating will be missing. For all of the missing ratings, estimate the rating, or score item k for judge j. Use the scores to estimate the top S scoring items that each judge has not rated.

16. 16 Netflix™Movie Rating

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18. 18 Applications of Recommender Systems For customers visiting a retail Web site, use information from previous purchases to recommend Books Music CDs Movies An “intelligent” music player: plays music specifically selected by user, when music has finished and user has not made a selection in over L seconds, the player makes a selection for the user based on previous selections the user has made. A news service that provides a personalized custom virtual newspaper to the subscriber based on past news article preferences. (These are usually content-based rather than collaborative.) continued...

19. 19 Applications of Recommender Systems Personalize a user’s home page with “interesting” links, with links based on a recommender system algorithm that recommends links that should be interesting to the user. Send a robot out looking for specific information, score each Web page using a recommender algorithm, and then return the K most interesting Web pages sorted by descending score (search engine applications). Index a library of information based on recommender system scores.

20. 20 Issues Some applications will not have ratings, but rather 0/1 or No/Yes settings, for example, Yes, the customer has purchased the title, or No, the customer has not purchased the title. Customer preferences may change over time, or a customer may discover a new artist, so re-training at regular intervals will be required for many applications.

21. 21 A Conditional Frequency Approach This method may be the current favorite, but it is generally considered to be inferior to more sophisticated approaches. Obtain a subset of your recommender data that only has customers who purchased the given item, call it item K. Ratings are 0 (did not buy) or 1 (bought). Derive the frequency distribution by all other titles. Sort from highest frequency to lowest. Recommend a set number of the highest frequency items. Most Web sites present the top 3 or the top 5 most frequent items. Example: Amazon.com