1. Machine Learned Relevance at a Large Scale Search Engine
Salford Analytics and Data Mining Conference 2012

2. Machine Learned Relevance at a Large Scale
Search Engine
Salford Data Mining – May 25, 2012
Presented by:
Dr. Eric Glover – eric@quixey.com
Dr. James Shanahan – james.shanahan@gmail.com

3. About the Authors
 James G. Shanahan - PhD in Machine Learning University of Bristol, UK
– 20+ years in the field AI and information science
– Principal and Founder, Boutique Data Consultancy
• Clients include: Adobe, Digg, SearchMe, AT&T, Ancestry SkyGrid, Telenav
– Affiliated with University of California Santa Cruz (UCSC)
– Adviser to Quixey
– Previously
• Chief Scientist, Turn Inc. (A CPX ad network, DSP)
• Principal Scientist, Clairvoyance Corp (CMU spinoff)
• Co-founder of Document Souls (task centric info access system)
• Research Scientist, Xerox Research (XRCE)
• AI Research Engineer, Mitsubishi Group

4. About the Authors
 Eric Glover - PhD CSE (AI) From U of M in 2001
– Fellow at Quixey, where among other things, he focuses on the architecture
and processes related to applied machine learning for relevance and
evaluation methodologies
– More than a dozen years of Search Engine experience including: NEC Labs, Ask
Jeeves, SearchMe, and own startup.
– Multiple relevant publications ranging from classification to automatically
discovering topical hierarchies
– Dissertation studied Personalizing Web Search through incorporation of user-
preferences and machine learning
– More than a dozen filed patents

5. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

6. Google

7. Search Engine: SearchMe
Search engine: lets you see and hear what you're searching for

8. 6 Steps to MLR in Practice
6
Deploy System in the wild (and
test)
5
Interpret and Evaluate
discovered knowledge
4
Modeling: Extract Patterns/Models
3
Feature Engineering
2
Collect requirements, and Data
1
Understand the domain and Systems Modeling is inherently
Define problems interactive and iterative.

9. How is ML for Search Unique
 Many Machine Learning (ML) systems start with source data
– Goal is to analyze, model, predict
– Features are often pre-defined, in a well-studied area
 MLR for Search Engines is different from many other ML applications:
– Does not start with labeled data
• Need to pay judges to provide labels
– Opportunity to invent new features (Feature Engineering)
– Often require real-time operation
• Processing tens of billions of possible results, microseconds matter
– Require domain-specific metrics for evaluation

10. If we can’t measure “it”, then…
 ….we should think twice about doing “it”
 Measurement has enabled us to compare systems
and also to machine learn them
 Search is about measurement, measurement and
measurement

11. Improve in a Measured Way

12. From Information Needs Queries
 The idea of using computers to search for relevant pieces of information was
popularized in the article As We May Think by Vannevar Bush in 1945
 An information need is an individual or group's desire to locate and obtain
information to satisfy a conscious or unconscious need.
 Within the context of web search information needs are expressed as textual
queries (possibly with constraints)
 E.g., “Analytics Data Mining Conference” program
 Metric: “Relevance” as a measure of how well is a system performing

13. Relevance is a Huge Challenge
 Relevance typically denotes how well a retrieved object (document) or set of objects
meets the information need of the user.
 Relevance is often viewed as multifaceted.
– A core facet of relevance relates to topical relevance or aboutness,
• i.e., to what extent the topic of a result matches the topic of the query or
information need.
• Another facet of relevance is based on user perception, and sometimes referred
to as user relevance; it encompasses other concerns of the user such as
timeliness, authority or novelty of the result
 In local search type queries, yet another facet of relevance that comes into play is
geographical aboutness,
– i.e., to what extent the location of a result, a business listing, matches the location of
the query or information need

14. From Cranfield to TREC
 Text REtrieval
Conference/Competition
– http://trec.nist.gov/
– Run by NIST (National Institute of
Standards & Technology)
 Started in 1992
 Collections: > 6 Gigabytes (5
CRDOMs), >1.5 Million Docs
– Newswire & full text news (AP,
WSJ, Ziff, FT)
– Government documents
(federal register, Congressional
Record)
– Radio Transcripts (FBIS)
– Web “subsets”
– Tweets

15. The TREC Benchmark
 TREC: Text REtrieval Conference (http://trec.nist.gov/) Originated from the
TIPSTER program sponsored by Defense Advanced Research Projects Agency
(DARPA).
 Became an annual conference in 1992, co-sponsored by National Institute of
Standards and Technology (NIST) and DARPA.
 Participants are given parts of a standard set of documents and TOPICS (from
which queries have to be derived) in different stages for training and testing.
 Participants submit the P/R values for the final document and query corpus
and present their results at the conference.
15

16. User’s
Information
Need Collections
Pre-process
text input
Parse Query Index
Match
Query Reformulation

17. User’s
Information
Need Collections
Pre-process
text input
Parse Query Index
Rank or Match
Evaluation
Query Reformulation

18. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

19. Difficulties in Evaluating IR Systems
 Effectiveness is related to the relevancy of the set of returned
items.
 Relevancy is not typically binary but continuous.
 Even if relevancy is binary, it can be a difficult judgment to make.
 Relevancy, from a human standpoint, is:
– Subjective: Depends upon a specific user’s judgment.
– Situational: Relates to user’s current needs.
– Cognitive: Depends on human perception and behavior.
– Dynamic: Changes over time.

20. Relevance as a Measure
Relevance is everything!
 How relevant is the document retrieved
– for the user’s information need.
 Subjective, but one assumes it’s measurable
 Measurable to some extent
– How often do people agree a document is relevant to a query
• More often than expected
 How well does it answer the question?
– Complete answer? Partial?
– Background Information?
– Hints for further exploration?

21. What to Evaluate?
What can be measured that reflects users’ ability to use
system? (Cleverdon 66)
– Coverage of Information
– Form of Presentation
– Effort required/Ease of Use
– Time and Space Efficiency
– Effectiveness
 Recall
– proportion of relevant material actually retrieved
 Precision
– proportion of retrieved material actually relevant
 Typically a 5-point scale is used 5=best, 1=worst

22. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

23. Data Collection is a Challenge
 Most search engines do not start with labeled data (relevance judgments)
 Good labeled data is required to perform evaluations and perform learning
 Not practical to hand-label all possibilities for modern large-scale search engines
 Using 3rd party sources such as Mechanical Turk is often very noisy/inconsistent
 Data collection is non-trivial
– A custom system (specific to the domain) is often required
– Phrasing of the “questions”, options (including a skip option), UI design and
judge training are critical to increase the chance of consistency
 Can leverage judgment collection to aid in feature engineering
– Judges can provide reasons and observations

25. Relevance/Usefulness/Ranking
 Web Search: topical relevance or aboutness, trustability of source
 Local Search: topical relevance and geographical applicability
 Functional App Search:
– Task relevance – User must be convinced app results can solve need
– Finding the “best” apps that address the users task needs
– Very domain and user specific
 Advertising
– Performance measure – expected revenue P(click) * revenue(click)
– Consistency with user-search (showing irrelevant ads hurts brand)

26. Commonly used Search Metrics
 Early search systems used binary judgments (relevant/not relevant) and evaluated
based on precision and recall
– Recall difficult to assess for large sets
 Modern search systems often use DCG or nDCG:
– Easy to collect and compare large sets of “independent judgments”
• Independent judgments map easily to MSE minimization learners
– Relevance is not binary, and depends on the order of results
 Other measures exist
– Subjective “how did I do”, but these are difficult to use for MLR or compare
– Pairwise comparison – measure number of out-of order pairs
• Lots of recent research on pairwise based MLR
• Most companies use “independent judgments”

27. Metrics for Web Search
 Existing metrics limited such as Precision and Recall
– Not always clear-cut binary decision: relevant vs. not relevant
– Not position sensitive:
p: relevant, n: not relevant
ranking 1: p n p n n
ranking 2: n n n p p
 How do you measure recall over the whole web?
– How many of the potentially billions results will get looked at? Which ones actually need to be good?
 Normalized Discounted Cumulated Gain (NDCG)
– K. Jaervelin and J. Kekaelaeinen (TOIS 2002)
– Gain: relevance of a document is no longer binary
– Sensitive to the position of highest rated documents
• Log-discounting of gains according to the positions
– Normalize the DCG with the “ideal set” DCG (NDCG)

28. Cumulative Gain
 With graded relevance relevance
(gain)
n doc # CG n
judgments, we can compute 1 588 1.0 1.0
2 589 0.6 1.6
the gain at each rank. 3 576 0.0 1.6
 Cumulative Gain at rank n: 4
5
590
986
0.8
0.0
2.4
2.4
6 592 1.0 3.4
7 984 0.0 3.4
8 988 0.0 3.4
9 578 0.0 3.4
10 985 0.0 3.4
(Where reli is the graded relevance 11 103 0.0 3.4
of the document at position i) 12 591 0.0 3.4
13 772 0.2 3.6
14 990 0.0 3.6
28

29. Discounting Based on Position
rel
 Users care more about high- n doc # (gain) CG n logn DCG n
1 588 1.0 1.0 - 1.00
ranked documents, so we 2 589 0.6 1.6 1.00 1.60
3 576 0.0 1.6 1.58 1.60
discount results by 4 590 0.8 2.4 2.00 2.00
1/log2(rank) 5 986 0.0 2.4 2.32 2.00
6 592 1.0 3.4 2.58 2.39
7 984 0.0 3.4 2.81 2.39
8 988 0.0 3.4 3.00 2.39
 Discounted Cumulative Gain: 9 578 0.0 3.4 3.17 2.39
10 985 0.0 3.4 3.32 2.39
11 103 0.0 3.4 3.46 2.39
12 591 0.0 3.4 3.58 2.39
13 772 0.2 3.6 3.70 2.44
14 990 0.0 3.6 3.81 2.44
29

30. Normalized Discounted Cumulative Gain
(NDCG)
 To compare DCGs, normalize values so that a ideal ranking would have a
Normalized DCG of 1.0
 Ideal ranking:
rel rel
(gain) n doc # (gain) CG n logn IDCG n
n doc # CG n logn DCG n
1 588 1.0 1.0 0.00 1.00 1 588 1.0 1.0 0.00 1.00
2 589 0.6 1.6 1.00 1.60 2 592 1.0 2.0 1.00 2.00
3 576 0.0 1.6 1.58 1.60 3 590 0.8 2.8 1.58 2.50
4 590 0.8 2.4 2.00 2.00 4 589 0.6 3.4 2.00 2.80
5 986 0.0 2.4 2.32 2.00 5 772 0.2 3.6 2.32 2.89
6 592 1.0 3.4 2.58 2.39 6 576 0.0 3.6 2.58 2.89
7 984 0.0 3.4 2.81 2.39 7 986 0.0 3.6 2.81 2.89
8 988 0.0 3.4 3.00 2.39 8 984 0.0 3.6 3.00 2.89
9 578 0.0 3.4 3.17 2.39 9 988 0.0 3.6 3.17 2.89
10 985 0.0 3.4 3.32 2.39 10 578 0.0 3.6 3.32 2.89
11 103 0.0 3.4 3.46 2.39 11 985 0.0 3.6 3.46 2.89
12 591 0.0 3.4 3.58 2.39 12 103 0.0 3.6 3.58 2.89
13 772 0.2 3.6 3.70 2.44 13 591 0.0 3.6 3.70 2.89
14 990 0.0 3.6 3.81 2.44 14 990 0.0 3.6 3.81 2.89
30

31. Normalized Discounted Cumulative Gain
(NDCG)
rel
n doc # (gain) DCG n IDCG n NDCG n
 Normalize by DCG of the ideal
1 588 1.0 1.00 1.00 1.00
ranking: 2 589 0.6 1.60 2.00 0.80
3 576 0.0 1.60 2.50 0.64
4 590 0.8 2.00 2.80 0.71
5 986 0.0 2.00 2.89 0.69
6 592 1.0 2.39 2.89 0.83
7 984 0.0 2.39 2.89 0.83
 NDCG ≤ 1 at all ranks 8 988 0.0 2.39 2.89 0.83
 NDCG is comparable across 9 578 0.0 2.39 2.89 0.83
10 985 0.0 2.39 2.89 0.83
different queries 11 103 0.0 2.39 2.89 0.83
12 591 0.0 2.39 2.89 0.83
13 772 0.2 2.44 2.89 0.84
14 990 0.0 2.44 2.89 0.84
31

32. Machine Learning Uses in Commercial SE
 Query parsing
 SPAM Classification
 Result Categorization
 Behavioral Categories
 Search engine results ranking

33. 6 Steps to MLR in Practice
6
Deploy System in the wild (and
test)
5
Interpret and Evaluate discovered
knowledge
4
Modeling: Extract Patterns/Models
3
Feature Engineering
2
Collect requirements, and Data
1
Understand the domain and Systems Modeling is inherently
Define problems
interactive and iterative

34. In Practice
 QPS, Deploy model
 Imbalanced data
 Relevance changes over time; non-stationary behavior
 Speed, accuracy, (SVMs,)
 Practical : Grid search, 8-16 nodes, 500 trees
 million of records, Interactions
Variable selection: 1000  100s variables, add random variables
 ~6 weeks cycle
 Training time is days; lab evaluation is weeks; live AB testing
 Why Treenet? No missing values, Categorical variables

35. MLR – Typical Approach by Companies
1. Define goals and “specific problem”
2. Collect human judged training data:
– Given a large set of <query, result> tuples
• Judges rate “relevance” on a 1 to 5 scale (5=“perfect”,
1=“worst”)
3. Generate training data from the provided <query, result> tuples
– <q,r>  Features, Input to model is: <F,judgment>
4. Train model typically minimize MSE (Mean Squared Error)
5. Lab evaluation using DCG-type metrics
6. Deploy model in a test system and evaluate

36. MLR Training Data
1. Collect human judged training data:
Given a large set of <query, result> tuples
Judges rate “relevance” on a 1 to 5 scale (5=“perfect”, 1=“worst”)
2. Featurize the training data from the provided <query, result> tuples
<q,r>  Features, Input to model is: <F,judgment>
InstanceAttr x0 x1 x2 … xn Label
<query1, Doc1> 1 3 0 .. 7 4
<query1, Doc2> 1 5
… … … … … … …
<queryn, Docn> 1 0 4 ... 8 3

37. The Evaluation Disconnect
 Evaluation in a supervised learner tries to minimize MSE of the targets
– for each tuple Fi,xi the learner predicts a target yi
• Error is f(yi – xi) – typically (yi – xi) ^2
• Optimum is some function of the “errors” – i.e. try minimize total error
 Evaluation of the deployed model is different evaluation of the learner - typically
DCG or nDCG
 Individual result error calculation is different from error based on result ordering
– A small error between predicted target for a result could have substantial
impact on result ordering – likewise, the “best result ordering” might not
exactly match the predicted targets for any results
– An affine transform of the targets produces no change to DCG, but large
change to calculated MSE

38. From Grep to Machine Learnt Ranking
Relative Performance
??
Personalization,
(e.g., DCG)
Social
Machine Learning,
Behavioral Data
Graph-Features,
Language Models
Boolean,VSM,
TF_IDF
Pre-1990s 1990s 2000s 2010s

39. Real World MLR Systems
 SearchMe was a visual/media search engine – about 3 Billion pages in index, and
hundreds of unique features used to predict the score (and ultimately rank results).
Results could be video, audio, images, or regular web pages.
– The goal was for a given input query, return the best ordering of relevant results –
in an immersive UI (mixing different results types simultaneously)
 Quixey – Functional App Search™ - over 1M apps, many sources of data for each app
(multiple stores, reviews, blog sites, etc…) – goal is given a “functional query” i.e. “a
good offline san diego map for iphone” or “kids games for android”– find the most
relevant apps (ranked properly)
– Dozens of sources of data for each app, many potential features used to:
• Predict “quality”, “text relevance” and other meta-features
• Calculate a meaningful score used to make decisions by partners
• Rank-order and raw score matter (important to know “how good” an app is)
 Local Search (Telenav, YellowPages)

40. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

41. Quixey: What is an App?
 An app is a piece of computer software designed to help a user perform specific
tasks.
– Contrast with systems software and middleware
 Apps were originally intended for productivity
– (email, calendar and contact databases), but consumer and business demand
has caused rapid expansion into other areas such as games, factory
automation, GPS and location-based services, banking, order-tracking, and
ticket purchases
 Run on various devices (phones, tablets, game consoles, cars)

42. My house is awash with platforms

43. My car...
NPR programs such as Car Talk are available 24/7 on the NPR
News app for Ford SYNC

44. My life...

46. ©

47. Own “The Millionaires App" for $1,000
 .

48. Law Students App
 ..

49. Apps for Pets
 ..

50. Pablo Picatso!
 ..

51. 50 Best iPhone Apps 2011 [Time]
Games On the Go Lifestyle Music & Entertainment Social
Angry Birds Kayak Amazon Photography Netflix Facebook
Scrabble Yelp Epicurious Mog IMDb Twitter
Plants v. Zombies Word Lens Mixology Pandora ESPN Scorecenter Google
Doodle Jump Weather Channel Paypal SoundHound Instapaper AIM
Fruit Ninja OpenTable Shop Savvy Bloom Kindle Skype
Cut the Rope Wikipedia Mint Camera+ PulseNews Foursquare
Pictureka Hopstop WebMD Photoshop Express Bump
Wurdle AroundMe Lose It! Hipstamatic
GeoDefense Google Earth Springpad Instagram
Swarm Zipcar ColorSplash
[http://www.time.com/time/specials/packages/completelist/0,29569,204
4480,00.html#ixzz1s1pAMNWM]

52. ©

53. Examples of Functional Search™
©

56. App World: Integrating Multi-Data Sources
A1 App Store A1
App Catalog A5
A2 1
A3 A7
Blogs
A2
A4
App Store
2
? blah blah Angry Birds
A5
?
A5 App Store App Review Site
A7 3 blah blah Learn Spanish
A8 App
Developer
Developer
Homepage
…

57. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

58. Search Architecture (Online)
Query
query DBQ = data storage queries
Processing
Offline
Indexes Processing
Data
Feature and Data
storage Data Building
ML
Simple
Models Scoring
(set reducer)
Shown Result Result Consideration Feature
Results Sorting Scoring Set Generation

59. Architecture Details
Online Flow:
1. Given a “query” generate Query-specific features, Fq
2. Using Fq generate appropriate “database queries”
3. Cheaply pare down initial possible results
4. Obtain result features Fr for remaining consideration set
5. Generate query-result features Fqr for remaining consideration set
6. Given all features score each result (assuming independent scoring)
7. Present and organize the “best results” (not nesc. linearized by score)

60. Examples of Possible Features
 Query features:
– popularity/frequency of query
– number of words in query, individual POS tags per term/token
– collection term-frequency information (per term/token)
– Geo-location of user
 Result features
– (web) – in-links/page-rank, anchortext match (might be processed with query)
– (app) – download rate, app-popularity, platform(s), star-rating(s), review-text
– (app) – ML-Quality score, etc..
 Query-result features
– BM-25 (per text-block)
– Frequency in specific sections – lexical similarity query to title
– etc…

61. Features Are Key
 Typically MLR systems use both textual and non-textual features:
• What makes one app better than another?
• Text-match alone insufficient
• Popularity alone insufficient
 No single feature or simple combination is sufficient
 At both SearchMe and Quixey we built learned “meta-features” (next slide)
non-title freq of
query: Games Title Text Match "game" App Popularity How good for query
Angry Birds low high Very high very high
Sudoku (genina.com) low low high high
PacMan low high high high
Cave Shooter low/medium medium low medium
Stupid Maze Game very high medium very low low

62. Features Are Key: Learned Meta-Features
 Meta-features can capture multiple simple features into fewer “super-features”
 SearchMe: SpamScore, SiteAuthority, Category-related
 Quixey: App-Quality, TextMatch (as distinct from overall-relevance)
 SpamScore and App-Quality are complex learned meta-features
– Potentially hundreds of “smaller features” feed into simpler model
– SpamScore considered – average-pageRank, num-ads, distinct
concepts, several language-related features
– App-Quality is learned (TreeNet) – designed to be resistant to gaming
• An app developer might pay people to give high-ratings
• Has a well defined meaning

63. Idea of Metafeatures (Example)
 In this case – each Metafeature is independently solved on different training data
Final Model Many data points (expensive)
… Many complex trees
Judgments prone to human errors
F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
VS
Explicit human decided metafeatures
Final Model produces simpler, faster models
MF1 MF2 MF3 requires fewer total training points
… Humans can define metafeatures to
F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 minimize human errors, and possibly
use different targets

64. Data and Feature Engineering are Key!
 Selection of “good” query/result pairs for labeling, and good metafeatures
– Should cover various areas of the sub-space (i.e. popular and rare queries)
– Be sure to only pick examples which “can be learned” and are representative
• Misspellings are a bad choice if no spell-corrector
• “exceptions” - i.e. special cases (i.e. Spanish results) for an English engine
are bad and should be avoided unless features can capture this
– Distribution is important
• Bias the data to focus on business goals
– If the goal is be the best for “long queries” have more “long queries”
 Features are critical – must be able to capture the variations (good metafeatures)
 Feature engineering is probably the single most important (and most difficult)
aspect of MLR

65. Applying TreeNet for MLR
 Starting with a set of query, result pairs obtain human judgments [1-5], and features
– 5=perfect, 1=worst (maps to target [0-1]
Query, Result, Judgment
Query, Result, Features
q1,r1, 2
q1,r1, f1,1, f1,2,f1,3,…,f1,n Candidate Models:
q1,r2, 5
q1,r2, f2,1, f2,2,f2,3,…,f2,n TreeNet M1, M2, M3, ….
q1,r3, 2
…
q2,r1, 4
…
Candidate Model: M
Results from Test Queries
Test
Search q1  r1,1 r1,2 … Human DCG
Queries
Engine q2  r2,1 r2,2 … Judgments Calculation
(q1,…qn)
…

67. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

68. Choosing the Best Model - Disconnect
 TreeNet uses a mean-squared error minimization
– The “best” model is the one with the lowest MSE where error is:
• abs(target – predicted_score)
– Each result is independent
 DCG minimizes rank-ordering error
– The ranking is query-dependent
 Might require evaluating several TreeNet models before a real DCG improvement
– Try new features,
– TreeNet options (learn rate, max-trees), change splits of data
– Collect more/better data (clean errors), consider active learning

69. Assumptions Made (Are there choices)
 MSE is used because the input data is independent judgment pairs
 Assumptions of consistency over time and between users (stationarity of
judgments)
– Is Angry Birds v1 a perfect score for “popular game” in 10 years?
– Directions need to be very clear to ensure user consistency
• Independent model assumes all users are consistent with each other
 Collect judgments in a different form:
– Pairwise comparisons <q1,r1> is better than <q1,r2>, etc…
– Evaluate a “set” of results
– Use a different scale for judgments which is more granular
– Full-ordering (lists)

70. Other Ways to do MLR
 Changing data collection:
– Use inferred as opposed to direct data
• Click/user behavior to infer relevance targets
– From independent judgments to pairwise or listwise
 Pairwise SVM:
– R. Herbrich, T. Graepel, K. Obermayer. “Support Vector Learning for Ordinal Regression.” In
Proceedings of ICANN 1999.
– T. Joachims, “A Support Vector Method for Multivariate Performance Measures.” In Proceedings of
ICML 2005. (http://www.cs.cornell.edu/People/tj/svm_light/svm_perf.html)
 Listwise learning
– LambdaRank, Chris Burghes et al, 2007
– LambdaMART, Qiang Wu, Chris J.C. Burges, Krysta M. Svore and Jianfeng Gao,
2008

71. Talk Outline
 Introduction: Search and Machine Learned Ranking
 Relevance and evaluation methodologies
 Data collection and metrics
 Quixey – Functional Application Search™
 System Architecture, features, and model training
 Alternative approaches
 Conclusion

72. Conclusion
 Machine Learning is very important to Search
– Metafeatures reduce model complexity and lower costs
• Divide and conquer (parallel development)
– MLR – is real, and is just one part of ML in search
 Major challenges include data collection and feature engineering
– Must pay for data – non-trivial, but have a say in what you collect
– Features must be reasonable for given problem (domain specific)
 Evaluation is critical
– How to evaluate effectively is important to ensure improvement
– MSE vs DCG disconnect
 TreeNet can and is an effective tool for Machine Learning in Search

73. Quixey is hiring
 If you want a cool internship, or a great job, contact us afterwards or e-mail:
 jobs@quixey.com and mention this presentation

74. Questions
James_DOT_Shanahan_AT_gmail_DOT_com
Eric_AT_Quixey_DOT_com

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