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Catching the Drift: Learning Broad Matches from Clickthrough Data Sonal Gupta, Mikhail Bilenko, Matthew Richardson University of Texas at Austin, Microsoft.

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Presentation on theme: "Catching the Drift: Learning Broad Matches from Clickthrough Data Sonal Gupta, Mikhail Bilenko, Matthew Richardson University of Texas at Austin, Microsoft."— Presentation transcript:

1 Catching the Drift: Learning Broad Matches from Clickthrough Data Sonal Gupta, Mikhail Bilenko, Matthew Richardson University of Texas at Austin, Microsoft Research

2 Introduction  Keyword-based online advertising: bidded keywords are extracted from context  Context: query (search ads) or page (content ads)  Broad matching: expanding keywords via keyword-to-keywords mapping  Example: electric cars  tesla, hybrids, toyota prius, golf carts  Broad matching benefits advertisers (increased reach, less campaign tuning), users (more relevant ads), ad platform (higher monetization) Expanded Keywords kw 1 kw 11 kw 12  kw n kw n1 kw n2  Broad Match Expansion Ad Selection and Ranking Ad 1 Ad 2  Ad k Extracted Keywords Keyword Extraction kw 1 kw 2  kw n Query or Web Page Selected Ads

3 Identifying Broad Matches  Good keyword mappings retrieve relevant ads that users click  How to measure what is relevant and likely to be clicked?  Human judgments: expensive, hard to scale  Past user clicks: provide click data for kw → kw’ when user was shown ad(kw' ) in context of kw  Highly available, less trustworthy  What similarity functions may indicate relevance of kw → kw' ?  Syntactic (edit distance, TF-IDF cosine, string kernels, …)  Co-occurrence (in documents, query sessions, bid campaigns, …)  Expanded representation (search result snippets, category bags, …)

4 Approach  Task: train a learner to estimate p(click | kw → kw' ) for any kw → kw'  Data  triples from clickthrough logs, where kw → kw' was suggested by previous broad match mappings  Features  Convert each pair to a feature vector capturing similarities etc. (kw → kw') →  For each triple, create an instance: ( ϕ (kw, kw' ), click)  Learner: max-margin averaged perceptron (strong theory, very efficient) ϕ 1 (kw, kw' ) ϕ n (kw, kw' ) … where ϕ i (kw, kw' ) can be any function of kw, kw' or both

5 5 Example: Creating an Instance  Historical broad match clickthrough data: kw  kw'  ad(kw' )  click event digital slr  canon rebel  Canon Rebel Kit for $499  click seattle baseball  mariners tickets  Mariners season tickets  no click  Feature functions  Instances [ ], 1 [ ], 0 Original kwBroad match kw' ϕ1ϕ1 ϕ2ϕ2 ϕ3ϕ3 digital slrcanon rebel seattle baseballmariners tickets

6 Experiments  Data  2 months of previous broad match ads from Microsoft Content Ads logs  1 month for training, 1 month for testing  68 features (syntactic, co-occurrence based, etc.); greedy feature selection  Metrics  LogLoss:  LogLoss Lift: difference between obtained LogLoss and an oracle that has access to empirical p(click | kw → kw' ) in test set.  CTR and revenue results in live test with users

7 Results

8 Live Test Results  Use CTR prediction to maximize expected revenue  Re-rank mappings to incorporate revenue  +18% revenue, -2% CTR

9 Online Learning with Amnesia  Advertisers, campaigns, bidded keywords and delivery contexts change very rapidly: high concept drift  Recent data is more informative  Goal: utilize older data while capturing changes in distributions  Averaged Perceptron doesn’t capture drift  Solution: Amnesiac Averaged Perceptron  Exponential weight decay when averaging hypotheses

10 Results Model-LogLossLogL Lift Prior Feature Selection + Online Learning + Amnesia Online+Feature Selection, No Amnesia Online+Amnesia, No Feature Selection Feature Selection+Amnesia, Weekly Batch

11 Contributions and Conclusions learning broad matches from implicit feedback  Combining arbitrary similarity measures/features  Using clickthrough logs as implicit feedback  Amnesiac Averaged Perceptron  Exponentially weighted averaging: distant examples “fade out”  Online learning adapts to market dynamics

12 Thank You!

13 13 Features and Feature Selection  Co-occurrence feature examples:  User search sessions: keywords searched within 10 mins  Advertiser campaigns: keywords co-bidded by the same advertiser  Past clickthrough rates of original and broad matched keywords  Various syntactic similarities  Various existing broad matching lists  and so on…  Feature Selection:  A total of 68 features  Greedy feature selection

14 Additional Information  Estimation of expected value of click over all the ads shown for a broad match mapping E(p(click(ad(kw))|q))  Query Expansion vs. Broad Matching  Our broad matching algorithm can be extended for query expansion  But, broad matching is for a fixed set of bidded keywords  Forgetron vs. Amesiac Averaged Perceptron  Forgetron maintains a set of budget support vectors: stores examples explicitly and does not take into account all the data  AAP: weighted average over all the examples, no need to store examples explicitly

15 Results Model-LogLossLogL Lift Prior Feature Selection + Online Learning + Amnesia Online+Amnesia, No Feature Selection Feature Selection+Amnesia, Weekly Batch Online+Feature Selection, No Amnesia

16 16 Amnesiac Averaged Perceptron


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