Buzz Words Dunning Multi Modal Recommendations

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Multi-Modal Recommendations

Multiple Kinds of Behavior
for Recommending
Multiple Kinds of Things

What’s Up
 What is this multi-modal stuff?
 A simple recommendation architecture
 Some scary math
 Putting it into a deployable architecture
 Final thoughts

 Contact:
– tdunning@maprtech.com
– @ted_dunning
– @apachemahout
– @user-subscribe@mahout.apache.org
 Slides and such (available late tonight):
– http://www.slideshare.net/tdunning
 Hash tags: #bbuzz #mapr #recommendations

Recommendations
 Often known (inaccurately) as collaborative filtering
 Actors interact with items
– observe successful interaction
 We want to suggest additional successful interactions
 Observations inherently very sparse

Examples of Recommendations
 Customers buying books (Linden et al)
 Web visitors rating music (Shardanand and Maes) or movies
(Riedl, et al), (Netflix)
 Internet radio listeners not skipping songs (Musicmatch)
 Internet video watchers watching >30 s (Veoh)

What is this multi-modal stuff?
 But people don’t just do one thing
 One kind of behavior is useful for predicting other kinds
 Having a complete picture is important for accuracy
 What has the user said, viewed, clicked, closed, bought lately?

A simple recommendation architecture
 Look at the history of interactions
 Find significant item cooccurrence in user histories
 Use these cooccurring items as “indicators”
 For all indicators in user history, add up scores

Recommendation Basics
 History:
User Thing
1 3
2 4
3 4
2 3
3 2
1 1
2 1

 History as matrix:
 (t1, t3) cooccur 2 times,
 (t1, t4) once,
 (t2, t4) once,
 (t3, t4) once
t1 t2 t3 t4
u1 1 0 1 0
u2 1 0 1 1
u3 0 1 0 1

A Quick Simplification
 Users who do h
 Also do r
Ah
AT
Ah( )
AT
A( )h
User-centric recommendations
Item-centric recommendations

 Coocurrence
t1 t2 t3 t4
t1 2 0 2 1
t2 0 1 0 1
t3 2 0 1 1
t4 1 1 1 2

Problems with Raw Cooccurrence
 Very popular items co-occur with everything
– Welcome document
– Elevator music
 That isn’t interesting
– We want anomalous cooccurrence

 Coocurrence
t1 t2 t3 t4
t1 2 0 2 1
t2 0 1 0 1
t3 2 0 1 1
t4 1 1 1 2
t3 not t3
t1 2 1
not t1 1 1

Spot the Anomaly
 Root LLR is roughly like standard deviations
A not A
B 13 1000
not B 1000 100,000
A not A
B 1 0
not B 0 2
A not A
B 1 0
not B 0 10,000
A not A
B 10 0
not B 0 100,000
0.44 0.98
2.26 7.15

Root LLR Details
 In R
entropy = function(k) {
-sum(k*log((k==0)+(k/sum(k))))
}
rootLLr = function(k) {
sign = …
sign * sqrt(
(entropy(rowSums(k))+entropy(colSums(k))
- entropy(k))/2)
}
 Like sqrt(mutual information * N/2)
See http://bit.ly/16DvLVK

Threshold by Score
 Coocurrence
t1 t2 t3 t4
t1 2 0 2 1
t2 0 1 0 1
t3 2 0 1 1
t4 1 1 1 2

Threshold by Score
 Significant cooccurrence => Indicators
t1 t2 t3 t4
t1 1 0 0 1
t2 0 1 0 1
t3 0 0 1 1
t4 1 0 0 1

So Far, So Good
 Classic recommendation systems based on these approaches
– Musicmatch (ca 2000)
– Veoh Networks (ca 2005)
 Currently available in Mahout
– See RowSimilarityJob
 Very simple to deploy
– Compute indicators
– Store in search engine
– Works very well with enough data

What’s right
about this?

Virtues of Current State of the Art
 Lots of well publicized history
– Musicmatch, Veoh, Netflix, Amazon, Overstock
 Lots of support
– Mahout, commercial offerings like Myrrix
 Lots of existing code
– Mahout, commercial codes
 Proven track record
 Well socialized solution

What’s wrong
about this?

Too Limited
 People do more than one kind of thing
 Different kinds of behaviors give different quality, quantity and
kind of information
 We don’t have to do co-occurrence
 We can do cross-occurrence
 Result is cross-recommendation

Heh?

Symmetry Gives Cross Recommentations
Why just dyadic learning?
Why not triadic learning?Why not cross learning?
AT
A( )hBT
A( )h

For example
 Users enter queries (A)
– (actor = user, item=query)
 Users view videos (B)
– (actor = user, item=video)
 A’A gives query recommendation
– “did you mean to ask for”
 B’B gives video recommendation
– “you might like these videos”

The punch-line
 B’A recommends videos in response to a query
– (isn’t that a search engine?)
– (not quite, it doesn’t look at content or meta-data)

Real-life example
 Query: “Paco de Lucia”
 Conventional meta-data search results:
– “hombres del paco” times 400
– not much else
 Recommendation based search:
– Flamenco guitar and dancers
– Spanish and classical guitar
– Van Halen doing a classical/flamenco riff

Real-life example

Hypothetical Example
 Want a navigational ontology?
 Just put labels on a web page with traffic
– This gives A = users x label clicks
 Remember viewing history
– This gives B = users x items
 Cross recommend
– B’A = label to item mapping
 After several users click, results are whatever users think they
should be

Nice. But we
can do better?

Ausers
things

A1 A2
é
ë
ù
û
users
thing
type 1
thing
type 2

A1 A2
é
ë
ù
û
users
action1
item type1
action2
item type2

A1 A2
é
ë
ù
û
T
A1 A2
é
ë
ù
û=
A1
T
A2
T
é
ë
ê
ê
ù
û
ú
ú
A1 A2
é
ë
ù
û
=
A1
T
A1 A1
T
A2
AT
2A1 AT
2A2
é
ë
ê
ê
ù
û
ú
ú
r1
r2
é
ë
ê
ê
ù
û
ú
ú
=
A1
T
A1 A1
T
A2
AT
2A1 AT
2A2
é
ë
ê
ê
ù
û
ú
ú
h1
h2
é
ë
ê
ê
ù
û
ú
ú
r1 = A1
T
A1 A1
T
A2
é
ëê
ù
ûú
h1
h2
é
ë
ê
ê
ù
û
ú
ú

Summary
 Input: Multiple kinds of behavior on one set of things
 Output: Recommendations for one kind of behavior with a
different set of things
 Cross recommendation is a special case

Now again, without
the scary math

Input Data
 User transactions
– user id, merchant id
– SIC code, amount
– Descriptions, cuisine, …
 Offer transactions
– user id, offer id
– vendor id, merchant id’s,
– offers, views, accepts

Input Data
 User transactions
– user id, merchant id
– SIC code, amount
– Descriptions, cuisine, …
 Offer transactions
– user id, offer id
– vendor id, merchant id’s,
– offers, views, accepts
 Derived user data
– merchant id’s
– anomalous descriptor terms
– offer & vendor id’s
 Derived merchant data
– local top40
– SIC code
– vendor code
– amount distribution

Cross-recommendation
 Per merchant indicators
– merchant id’s
– chain id’s
– SIC codes
– indicator terms from text
– offer vendor id’s
 Computed by finding anomalous (indicator => merchant) rates

Search-based Recommendations
 Sample document
– Merchant Id
– Field for text description
– Phone
– Address
– Location

 Sample document
– Merchant Id
– Phone
– Address
– Location
– Indicator merchant id’s
– Indicator industry (SIC) id’s
– Indicator offers
– Indicator text
– Local top40

 Sample document
– Merchant Id
– Phone
– Address
– Location
– Indicator merchant id’s
– Indicator industry (SIC) id’s
– Indicator offers
– Indicator text
– Local top40
 Sample query
– Current location
– Recent merchant descriptions
– Recent merchant id’s
– Recent SIC codes
– Recent accepted offers
– Local top40

SolR
Indexer
SolR
Indexer
Solr
indexing
Cooccurrence
(Mahout)
Item meta-
data
Index
shards
Complete
history

SolR
Indexer
SolR
Indexer
Solr
search
Web tier
Item meta-
data
Index
shards
User
history

 Contact:
– tdunning@maprtech.com
– @ted_dunning
– @apachemahout
– @user-subscribe@mahout.apache.org
 Slides and such (available late tonight):
– http://www.slideshare.net/tdunning
 Hash tags: #bbuzz #mapr #recommendations
 We are hiring!

Objective Results
 At a very large credit card company
 History is all transactions, all web interaction
 Processing time cut from 20 hours per day to 3
 Recommendation engine load time decreased from 8 hours to 3
minutes
 Recommendation quality increased visibly

Thank You

Buzz Words Dunning Multi Modal Recommendations

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