sparking science up with research recommendations

Sparking Science up with Research Recommendations

Maya Hristakeva @mayahhf

Overview •  What is Mendeley Suggest?

•  Computation Layer

•  Conclusions

Read &

Organize

Search &

Discover

Collaborate &

Network

Experiment &

Synthesize

Mendeley builds tools to help researchers …

Being the best researcher you can be! •  Good researchers are on top of their game •  Large amount of research produced •  Takes time to get what you need

•  Help researchers by recommending relevant research

Mendeley Suggest Personalized Article

Recommender

Recommender System Components

information flow (components often built in parallel)

Data (Feature

Engineering) Algorithms Business Logic

and Analytics User Experience

Mendeley Suggest Components (Past)


Data (Feature



Mendeley Suggest Components (Present)


Data (Feature



Mendeley Suggest Components (Goal)


Data (Feature




•  Computation Layer –  Algorithms

–  Evaluation

–  Implementations & Performance

•  Conclusions

Personalized Article Recommendations Input: User libraries

Output: Suggested articles to read

Algorithms: •  Collaborative Filtering

–  Item-based

–  User-Based

–  Matrix Factorization

•  Content-based

Item-based Collaborative Filtering Recommend articles that are similar to the ones you read

–  Similarity is based on article co-occurrences in users’ libraries –  “Users who read x also read y”

User-based Collaborative Filtering

Find users who have similar appreciation for articles as you –  Similarity is based on users’ libraries overlap

Recommend new articles based on what the users similar to you read

–  “Users similar to you (based on a, b, c) also read x”

Matrix Factorization CF

2 4 5

5 4 1

5 ? 2

1 5 4

4 2

4 5 1

U n x k

V k x m

fij= <Ui*,V*j> E(U,V) = L(Xij, fij) + R(U,V)

X n x m



–  Evaluation

–  Implementations

•  Conclusions

Performance Costly & Good Costly & Bad

Cheap & Good Cheap & Bad

Costly & Good Costly & Bad


Performance

How to measure quality? •  Offline Evaluation

–  Parameter sweep is quick –  Don’t offend real users

•  Methodology –  n-fold cross-validation –  time-based validation

•  Metrics –  precision, recall and f-measure –  AUC (area under roc curve), NDCG (normalized discounted cumulative gain)



–  Evaluation

–  Implementations

•  Conclusions

Implementations Mahout

(Hadoop)Mendeley (Hadoop)

Mahout (Spark)

Mendeley (Spark)

MLlib (Spark)

Item-based CF

User-based CF

Matrix Factorization

Setup •  EMR Cluster

–  Master: 1 x r3.xlarge instance (4 core, 32GB) –  Core: 10 x r3.2xlarge instances (8 core, 64GB)

•  Data: user libraries –  15mil documents >>> 1mil users –  150mil interactions

•  Offline Evaluation –  Methodology: time-based evaluation –  Metric: precision@10



Mahout (Spark)

Mendeley (Spark)

MLlib (Spark)

Item-based CF

User-based CF


Apache Mahout •  Mahout (out-of-the-box)

–  Item-based CF •  org.apache.mahout.cf.taste.hadoop.item.RecommenderJob

–  ALS Matrix Factorization •  org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJob

•  org.apache.mahout.cf.taste.hadoop.als.RecommenderJob

•  Implemented User-based CF on top of Mahout at Mendeley



Orig. item-based mahout

Tuned item-based mahout

-0.5K (-60%)

Performance

~$125




Tuned item-based mahout

-0.5K (-60%)

Orig. user-based mahout

Tuned user-based mahout

-0.1K (-40%)

Performance

~$125




Tuned item-based mahout Orig. user-based

mahout

Tuned user-based mahout

+150%

-0.2K (-55%)

-0.7K (-82%)

Performance

~$125

Mahout Performance •  Mahout’s recommender is already efficient

–  But your data may have unusual properties

•  We’ve got improvements by –  Tuning Hadoop’s mapper and reducer allocation over the Recommender Job steps –  Using an appropriate partitioner

•  Improve quality –  Mahout provides Item-based CF –  We have many more items than users –  Typically, user-based is more appropriate



Mahout (Spark)

Mendeley (Spark)

MLlib (Spark)

Item-based CF

User-based CF


Mahout Spark •  Co-occurrence Recommenders with Spark

–  Item-Item similarity •  mahout spark-itemsimilarity

SimilarityAnalysis.cooccurrencesIDSs(ratings, …)

–  User-User similarity •  mahout spark-rowsimilarity

SimilarityAnalysis.rowSimilarityIDSs(ratings, …)

•  Only supports Boolean data and log-likelihood similarity

•  Does not generate actual recommendations

Mahout Spark •  Could not get to run successfully on our data

•  Got further by tuning parameters but still failed with OOM –  spark.driver.maxResultSize

–  spark.kryoserializer.buffer.max

–  spark.default.parallelism

–  spark.storage.memoryFraction

•  Gave best runtime performance on MovieLens datasets



Mahout (Spark)

Mendeley (Spark)

MLlib (Spark)

Item-based CF

User-based CF


Mendeley Spark •  Started as hack-day project

–  Implement Item-based and User-based CF in Spark

•  Can be implemented in two steps 1.  Compute Item-Item or User-User Similarities

•  given user preferences

2.  Compute Recommendations •  given similarities and user preferences

Spark: Item-Item Similarity

Spark: Item-Based Recs



Tuned IB Mahout

Tuned UB Mahout

Orig. UB Spark

Performance

~$50



Tuned IB Mahout

Tuned UB Mahout

Orig. UB Spark

Tuned UB Spark

Tuned IB Spark

-0.1K (-40%)

Performance

~$50

Mendeley Spark Performance •  Spark implementation of User-based CF performs well

•  Managed to run variation of Item-based CF –  Uses fewer items per user to recommend similar items to –  Quality not impacted much

•  We’ve got improvements by tuning –  Resource allocation –  Parallelism –  http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-

part-2/



Mahout (Spark)

Mendeley (Spark)

MLlib (Spark)

Item-based CF

User-based CF


Spark MLlib DimSum •  DimSum: efficient algorithm for computing all-pairs similarity

–  “Dimension Independent Matrix Square using MapReduce” –  Contributed by Twitter

•  Replace similarity computation with DimSum –  Only supports cosine similarity

•  Does not generate actual recommendations –  Compute recommendations as before

MLlib DimSum Item-Item Similarity

MLlib DimSum User-User Similarity



Tuned IB Mahout

Tuned UB Mahout

Tuned UB Spark

Tuned IB Spark

UB DimSum Spark MLlib

Performance

~$50

Spark MLlib Matrix Factorization Implements alternating least squares (ALS) 1.  Compute Model 2.  Compute Recommendations



Tuned IB Mahout

Tuned UB Mahout

Tuned UB Spark

Tuned IB Spark


ALS Matrix Fact. Spark MLlib

-50%

Performance

~$50

MLlib Performance •  Provides good alternative for computing user-user similarities

–  Due to data sparsity, not getting big gains in runtime –  Only supports cosine similarity

•  Failed to compute item-item similarities –  Exceeds maximum allowed value of 2G for spark.kryoserializer.buffer.max

•  User-based CF outperforms ALS CF

•  Need scalable solution for generating recommendations based on ALS CF model



Mahout (Spark)

Mendeley (Spark)

MLlib (Spark)

Item-based CF

User-based CF



•  Computation Layer

•  Conclusions



Tuned IB Mahout

Tuned UB Mahout

Tuned UB Spark

Tuned IB Spark


ALS Matrix Fact. Spark MLlib

Performance

+100%

+150% ~$50

Mendeley Suggest Components (Future)


Data (Feature



Conclusions •  Mendeley Suggest is a personalized article recommender

•  Spark is good alternative to Mahout as computation layer –  Needs some love and tuning –  Much fewer lines of code – easier to maintain and extend

•  User-based can outperform item-based and matrix factorization

•  Save resources and money by understanding your data

•  Test offline before deploying –  but also need online tests to get real performance

Thank you! mendeley.com/suggest

sparking science up with research recommendations

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