Similarity at scale

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This is a presentation I gave at Hadoop Summit San Jose 2014, on doing fuzzy matching at large scale using combinations of Hadoop & Solr-based techniques.

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  • In nine years of using Hadoop & Solr, I've made a lot of mistakes
  • Open source is filled with key/value systems.
    My goal in the next three slides isn't to give a lecture on similarity.
    Covered in lots of detail by books, papers, etc.
    Providing context for discussion on the real-world problems and solutions
  • This text comes from two different versions of the Wikipedia page on Bosnia & Herzegovina.
    We read it for meaning, and that's similar - but how would a computer decide these are "similar"?
  • Looking at these two people, a person can say "yes, they're the same".
  • Looking at these two people, a person can say "They're likely to be the same".
    Bob vs. Robert, missing middle initial, no apartment, typo in street number, abbreviations, missing zip, etc.
  • Obviously a typical document can have thousands of unique words
    So very high dimensionality for the term vector
  • This assumes symmetry - the score of A compared to B is the same as B compared to A
  • This is from a module in the DataStax Solr course
    It uses real page-view data from the DataStax web site
  • Ted Dunning talks about this approach frequently.
    LLR is used determine which co-occurrences are sufficiently anomalous to be of interest as indicators
    Challenges in that RowSimilarityJob wants just integer ids for everything, so some back-and-forth conversion is neededPat Ferrel has a project that implements much of this approach.
  • We used Hadoop to process the original logs
    And we can use Hadoop to generate this co-occurrence matrix
    Then we use Solr/Lucene to search for items to recommend
  • It's amazing how often something like a phone number, or even an SSN, gets entered incorrectly.
  • Performance is mostly impacted by the complexity (# of fields) in the query.
    Typically a query is < 200ms.
  • Essentially we're trying to mimic much of what the more sophisticated matching does
    But without impacting search performance
    Within the constraints of Lucene/Solr
  • Really this is a generalization of the magic field approach.
    Trying to reduce the number of record-record comparisons.
  • Similarity at scale

    1. 1. 1 Copyright (c) 2014 Scale Unlimited. Similarity at Scale Fuzzy matching and recommendations using Hadoop, Solr, and heuristics Ken Krugler Scale Unlimited
    2. 2. 2 Copyright (c) 2014 Scale Unlimited. The Twitter Pitch Wide class of problems that rely on "good" similarity Fast Accurate Scalable Benefit from my mistakes Scale Unlimited - consulting & training Talking about solutions to real problems
    3. 3. 3 Copyright (c) 2014 Scale Unlimited. What are similarity problems? Clustering Grouping similar advertisers Deduplication Joining noisy sets of POI data Recommendations Suggesting pages to users Entity resolution Fuzzy matching of people and companies
    4. 4. 4 Copyright (c) 2014 Scale Unlimited. What is "Similarity"? Exact matching is easy(er) Accuracy is a given Fast and scalable can still be hard Lots of key/value systems like Cassandra, HBase, etc. Fuzzy matching is harder Two "things" aren't exactly the same Similarity is based on comparing features
    5. 5. 5 Copyright (c) 2014 Scale Unlimited. Between two articles? Features could be a bag of words Are these two articles the same? Bosnia is the largest geographic region of the modern state with a moderate continental climate, marked by hot summers and cold, snowy winters. The inland is a geographically larger region and has a moderate continental climate, bookended by hot summers and cold and snowy winters.
    6. 6. 6 Copyright (c) 2014 Scale Unlimited. What about now? Easy to create challenging situations for a person Which is an impossible problem for a computer Need to distinguish between "conceptually similar" and "derived from" Bosnia is the largest geographic region of the modern state with a moderate continental climate, marked by hot summers and cold, snowy winters. Bosnia has a warm European climate, though the summers can be hot and the winters are often cold and wet.
    7. 7. 7 Copyright (c) 2014 Scale Unlimited. Between two records? Features could be field values Are these two people the same? Name Bob Bogus Robert Bogus Address 220 3rd Avenue 220 3rd Avenue City Seattle Seattle State WA WA Zip 98104-2608 98104
    8. 8. 8 Copyright (c) 2014 Scale Unlimited. What about now? Need to get rid of false differences caused by abbreviations How does a computer know what's a "significant" difference? Name Bob Bogus Robert H. Bogus Address Apt 102, 3220 3rd Ave 220 3rd Avenue South City Seattle Seattle State Washington WA Zip 98104
    9. 9. 9 Copyright (c) 2014 Scale Unlimited. Between two users? Features could be... Items a user has bought Are these two users the same? User 1 User 2
    10. 10. 10 Copyright (c) 2014 Scale Unlimited. What about now? Need more generic features E.g. product categories User 1 User 2
    11. 11. 11 Copyright (c) 2014 Scale Unlimited. How to measure similarity? Assuming you have some features for two "things" How does a program determine their degree of similarity? You want a number that represents their "closeness" Typically 1.0 means exactly the same And 0.0 means completely different
    12. 12. 12 Copyright (c) 2014 Scale Unlimited. Jaccard Coefficient Ratio of number of items in common / total number of items Where "items" typical means unique values (sets of things) So 1.0 is exactly the same, and 0.0 is completely different
    13. 13. 13 Copyright (c) 2014 Scale Unlimited. Cosine Similarity Assume a document only has three unique words cat, dog, goldfish Set x = frequency of cat Set y = frequency of dog Set z = frequency of goldfish The result is a "term vector" with 3 dimensions Calculate cosine of angle between term vectors This is their "cosine similarity"
    14. 14. 14 Copyright (c) 2014 Scale Unlimited. Why is scalability hard? Assume you have 8.5 million businesses in the US There are N^2/2 pairs to evaluate≈ That's 36 trillion comparisons Sometimes you can quickly trim this problem E.g. if you assume the ZIP code exists, and must match Then this becomes about 4 billion comparisons But often you don't have a "magic" field
    15. 15. 15 Copyright (c) 2011-2014 Scale Unlimited. All Rights Reserved. Reproduction or distribution of this document in any form without prior written permission is forbidden. DataStax Web Site Page Recommender
    16. 16. 16 Copyright (c) 2014 Scale Unlimited. How to recommend pages? Besides manually adding a bunch of links... Which is tedious, doesn't scale well, and gets busy
    17. 17. 17 Copyright (c) 2014 Scale Unlimited. Can we exploit other users? Classic shopping cart analysis "Users who bought X also bought Y" Based on actual activity, versus (noisy, skewed) ratings
    18. 18. 18 Copyright (c) 2014 Scale Unlimited. What's the general approach? We have web logs with IP addresses, time, path to page 157.55.33.39 - - [18/Mar/2014:00:01:00 -0500] "GET /solutions/nosql HTTP/1.1" A browsing session is a series of requests from one IP address With some maximum time gap between requests Find sessions "similar to" the current user's session Recommend pages from these similar sessions
    19. 19. 19 Copyright (c) 2014 Scale Unlimited. How to find similar sessions? Create a Lucene search index with one document per session Each indexed document contains the page paths for one session session-1 /path/to/page1, /path/to/page2, /path/to/page3 session-2 /path/to/pageX, /path/to/pageY Search for paths from the current user's session
    20. 20. 20 Copyright (c) 2014 Scale Unlimited. Why is this a search issue? Solr (search in general) is all about similarity Find documents similar to the words in my query Cosine similarity is used to calculate similarity Between the term vector for my query and the term vector of each document
    21. 21. 21 Copyright (c) 2014 Scale Unlimited. What's the algorithm? Find sessions similar to the target (current user's) session Calculate similarity between these sessions and the target session Aggregate similarity scores for all paths from these sessions Remove paths that are already in the target session Recommend the highest scoring path(s)
    22. 22. 22 Copyright (c) 2014 Scale Unlimited. Why do you sum similarities? Give more weight to pages from sessions that are more similar Pages from more similar sessions are assumed to be more interesting
    23. 23. 23 Copyright (c) 2014 Scale Unlimited. What are some problems? The classic problem is that we recommend "common" pages E.g. if you haven't viewed the top-level page in your session But this page is very common in most of the other sessions So then it becomes one of the top recommended page But that generally stinks as a recommendation
    24. 24. 24 Copyright (c) 2014 Scale Unlimited. Can RowSimilarityJob help? Part of the Mahout open source project Takes as input a table of users (one per row) with lists of items Generates an item-item co-occurrence matrix Values are weights calculated using log-likelihood ratio (LLR) Unsurprising (common) items get low weights If we run it on our data, where users = sessions and items = pages We get page-page co-occurrence matrix Page 1 Page 2 Page 3 Page 1 2.1 0.8 Page 2 2.1 4.5 Page 3 0.8 4.5
    25. 25. 25 Copyright (c) 2014 Scale Unlimited. How to use co-occurrence? Convert the matrix into an index Each row is one Lucene document Drop any low-scoring entries Create list of "related" pages Search in Related Pages field Using pages from current session So Page 2 recommends Page 1 & 3 Page 1 Page 2 Page 3 Page 1 2.1 0.8 Page 2 2.1 4.5 Page 3 0.8 4.5 Related Pages Page 1 Page 2 Page 2 Page 1, Page 3 Page 3 Page 2
    26. 26. 26 Copyright (c) 2011-2014 Scale Unlimited. All Rights Reserved. Reproduction or distribution of this document in any form without prior written permission is forbidden. EWS Entity Resolution Entity Resolution
    27. 27. 27 Copyright (c) 2014 Scale Unlimited. What is Early Warning? Early Warning helps banks fight fraud It's owned by the top 5 US banks And gets data from 800+ financial institutions So they have details on most US bank accounts When somebody signs up for an account They need to quickly match the person to "known entities" And derive a risk score based on related account details
    28. 28. 28 Copyright (c) 2014 Scale Unlimited. Why do they need similarity? Assume you have information on 100s of millions of entities Name(s), address(es), phone number(s), etc. And often a unique ID (Social Security Number, EIN, etc) Why is this a similarity problem? Data is noisy - typos, abbreviations, partial data People lie - much fraud starts with opening an account using bad data
    29. 29. 29 Copyright (c) 2014 Scale Unlimited. How does search help? We can quickly build a list of candidate entities, using search Query contains field data provided by the client bank Significantly less than 1 second for 30 candidate entities Then do more precise, sophisticated and CPU-intensive scoring The end result is a ranked list of entities with similarity scores Which then is used to look up account status, fraud cases, etc.
    30. 30. 30 Copyright (c) 2014 Scale Unlimited. What's the data pipeline? Incoming data is cleaned up/normalized in Hadoop Simple things like space stripping Also phone number formatting ZIP+4 expansion into just ZIP plus full Other normalization happens inside of Solr This gets loaded into Cassandra tables And automatically indexed by Solr, via DataStax Enterprise ZIP+4 Terms 95014-2127 95014, 2127 Phone Terms 4805551212 480, 5551212
    31. 31. 31 Copyright (c) 2014 Scale Unlimited. What's the Solr setup? Each field in the index has very specific analysis Simple things like normalization Synonym expansion for names, abbreviations Split up fields so partial matches work At query time we can weight the importance of each field Which helps order the top N candidates similar to their real match scores E.g. an SSN matching means much more than a first name matching
    32. 32. 32 Copyright (c) 2011-2014 Scale Unlimited. All Rights Reserved. Reproduction or distribution of this document in any form without prior written permission is forbidden. Batch Similarity
    33. 33. 33 Copyright (c) 2014 Scale Unlimited. Can we do batch similarity? Search works well for real-time similarity But batch processing at scale maxes out the search system We can use two different techniques with Hadoop for batch SimHash - good for text document similarity Parallel Set-Similarity Joins - good for record similarity
    34. 34. 34 Copyright (c) 2014 Scale Unlimited. What is SimHash? Assume a document is a set of (unique) words Calculate a hash for each word Probability that the minimum hash is the same for two documents... ...is magically equal to the Jaccard Coefficient Term Hash bosnia 78954874223 is 53466156768 the 5064199193 largest 3193621783 geographic -5718349925
    35. 35. 35 Copyright (c) 2014 Scale Unlimited. What is a SimHash workflow? Calculate N hash values Easy way is to use the N smallest hash values Calculate number of matching hash values between doc pairs (M) Then the Jaccard Coefficient is M/N≈ Only works if N is much smaller than # of unique words in docs Implementation of this in cascading.utils open source project https://github.com/ScaleUnlimited/cascading.utils
    36. 36. 36 Copyright (c) 2014 Scale Unlimited. What is Set-Similarity Join? Joining records in two sets that are "close enough" aka "fuzzy join" Requires generation of "tokens" from record field(s) Typically words from text Simple implementation has three phases First calculate counts for each unique token value Then output <token, record> for N most common tokens of each record Group by token, compare records in each group
    37. 37. 37 Copyright (c) 2014 Scale Unlimited. How does fuzzy join work? For two records to be "similar enough"... They need to share one of their common tokens Generalization of the ZIP code "magic field" approach Basic implementation has a number of issues Passing around copies of full record is inefficient Too-common tokens create huge groups for comparison Two records compared multiple times
    38. 38. 38 Copyright (c) 2011-2014 Scale Unlimited. All Rights Reserved. Reproduction or distribution of this document in any form without prior written permission is forbidden. Summary
    39. 39. 39 Copyright (c) 2014 Scale Unlimited. The Net-Net Similarity is a common requirement for many applications Recommendations Entity matching Combining Hadoop with search is a powerful combination Scalability Performance Flexibility
    40. 40. 40 Copyright (c) 2014 Scale Unlimited. Questions? Feel free to contact me http://www.scaleunlimited.com/contact/ Take a look at Pat Ferrel's Hadoop + Solr recommender http://github.com/pferrel/solr-recommender Check out Mahout http://mahout.apache.org Read paper & code for fuzzyjoin project http://asterix.ics.uci.edu/fuzzyjoin/

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