Antonio Fernández Anta Joint work with Luis F. Chiroque, Héctor Cordobés,

Rafael A. García Leiva, Philippe Morere,

Lorenzo Ornella, Fernando Pérez, and Agustín Santos

•  Recommendation Engines (RE) suggest items to users •  RE are becoming highly popular in many different

contexts -  Shopping websites (Amazon),

-  Content distribution (Netflix, Spotify)

- Online social networks (Facebook, twitter, LikendIn)

•  Some authors talk about “the age of recommendation” versus the age of search (Chris Anderson, The Long Tail)

•  Most modern RE are based on collaborative filtering: Recommendations are based on

- Historic data

- Similarity between

users

- Similarity between

items

•  Multiple metrics to quantify similarity: Euclidean distance, Cosine similarity, Pearson correlation similarity, etc.

•  The users and items are typically connected as a bipartite graph

•  Considering users as nodes and items as hyperlinks (or vice versa) we obtain hypergraphs (that can be transformed into graphs of users or items)

•  Graph theory and network analysis concepts can be useful (as before)

- Google, Pagerank

- Natural Language Processing

•  We explore graph-based approaches for recommendation engines

•  Apply them to an ecosystem of smartphone apps.

- Apps are advertised in other

apps via banners

- Performance metric is

click-through rate (CTR)

•  Large (big) data available

- More than a billion records

- Millions of users

- Hundreds of items (apps)

•  Recommendation engines based on

- Collaborative filtering (in wide sense)

- Graph theory concepts

•  The engines were evaluated in the real world (and showed good performance)

•  Involves processing the historical data available with big data technologies: Hadoop, Elastic Map Reduce, Pig

•  The processing is done is a few hours thanks to these technologies

•  There are more that 100 GiB of historical data

- Millions of users

- 300 applications

- More than 1400 millions records

•  The data is in multiple tables

•  The first process is to “clean”

the data

•  This involves joining several

tables

•  The output of this process is a file with records that contain

- User

- Application advertised

- Running application (publisher app)

- Action (advertisement, click)

- Date and time

•  In MySQL we started in of the join operations (not the largest) and stopped it after 30 hours and no more than 15% completion

•  We have used Hadoop on Amazon Elastic Map Reduce and Pig scripts to process the data

•  The output has more that 700 million records

•  From the clean data the graphs used by the RE have been generated

•  This process is less time consuming since it typically involves data aggregation

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•  The graph that has apps as nodes and undirected links weighted by the number of common users

4

45

7

22

5

15

25

•  Shared users: The apps with largest number of users shared with the publisher app are preferred

4

45

7

22

5

15

25

Publisher app

recommendation

•  Filtering algorithm:

- Let v the binary vector of the requesting user apps, and M the adjacency matrix of the common users graph

- The apps whose position have the largest value in vTM are preferred

7

22

25

5 recommendation

25

27

7

•  Common users graph, modulated by age (user weight decreases exponentially with age)

Weight(app1,app2)= Σu δage(u)

•  Aged shared users: Same as shared users in the aged graph

•  Aged filtering: Same as shared users in the aged graph

•  The CTR graph is a directed graph with links weighted by the frequency of clicks in the banner of the application (head) in the publisher (tail)

4/12

4/50

7/14

22/40

5/20

6/10

25/50

•  Maxflow algorithm:

- Recommendation algorithm used to promote specific applications

- The apps with largest maxflow in the CTR graph to the promoted apps are preferred

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source

Promoted app

.6

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.08

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.35 recommendation

•  For reference there are two basic recommendation engines:

- Random: Engine that suggests random applications

- Static promotion: Returns always the promoted apps

•  The different algorithms have been tested over a week in the real system

•  These values improve over the current CTR

Algorithm CTR

Random 1.57%

Shared users 1.64%

Aged shared users 1.69%

Filtering 1.51%

Aged filtering 1.71%

Static promotion 1.45%

Maxflow 1.86%

Aging is useful

Global view

•  Graph analytics can be useful in the development of recommendation engines

•  Big data technology allowed us to process historical data and produce graphs

•  The graphs generated are small. They could be processed with classical technologies

•  Current map reduce technologies do not seem to be the solution for large graph analysis

•  Explore technologies that are more suited for large graph analytics:

- Graphlab, GraphChi

- Spark, GraphX

- Stratosphere, Flink, Spargel

•  Devise ways to process incremental data

•  Design and testing of new recommendation algorithms that use larger graphs

Thank you!