Watch-It-Next: A Contextual TV Recommendation System

Michal Aharon, Eshcar Hillel, Amit Kagian, Ronny Lempel, Hayim Makabee, Raz Nissim

Recommendation in Personal Devices and Accounts

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Challenge: Recommendations in Shared Accounts and Devices

“I am a 34 yo man who enjoys action and sci-fi movies. This is what my children have done to my netflix account”

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Our Focus: Recommendations for Smart TVs

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Main problems: Inferring who has

consumed each item in the past

Who is currently requesting the recommendations

“Who” can be a subset of users

Smart TVs can track what is being watched on them, but not who was watching.

Solution: Using Context

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Previous work: time of day

Context in this Work:Current Item Being Watched

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This Work: Contextual Personalized Recommendations

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WatchItNext problem: it is 8:30pm and “House of Cards” is on What should we recommend to be

watched next on this device?

Implicit assumption: there’s a good chance whoever is in front of the set now, will remain there

WatchItNext Inputs and Output

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Available programs, a.k.a. “line-up”

Ranked recommendations

A fundamental principle in recommender systems Taps similarities in patterns of

consumption/enjoyment of items by users Recommends to a user what users with detected

similar tastes have consumed/enjoyed

Collaborative Filtering

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Consider a consumption matrix R of users and items

ru,i=1 whenever person u consumed item i In other cases, ru,i might be person u’s rating on item i

The matrix R is typically very sparse …and often very large

Collaborative Filtering – Mathematical Abstraction

users

R =

Items

|U| x |I|

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Latent factor models (LFM): Map both users and items to some f-dimensional space Rf, i.e.

produce f-dimensional vectors vu and wi for each user and item Define rating estimates as inner products: qui = <vu,wi> Main problem: finding a mapping of users and items to the

latent factor space that produces “good” estimates

Collaborative Filtering – Matrix Factorization

users

R =

Items

≈

|U| x |I| |U| x f f x |I|

VW

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Main Contribution:“3-Way” Technique

Learn a standard matrix factorization model (LFM/LDA) When recommending to a device d currently watching

context item c, score each target item t as follows:S(t follows c|d) = j=1..k vd(j)*wc(j)*wt(j)

May require an additive shift to get rid of negative values. Score is high for targets that agree with both context and

device Results in “Sequential LFM/LDA” – a personalized contextual

recommender Again – no need to model context or change learning

algorithm; learn as usual, just apply change when scoring

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Data by the Numbers Training data: three months’ worth of viewership

data

Test Data: derived from one month of viewership data

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* Items are {movie, sports event, series} – not at the individual episode level

Devices Unique items* Triplets339647 17232 More than 19M

Setting Test Instances Average Line-up SizeHabitual ~3.8M 390

Exploratory

~1.7M 349

Metric: Avg. Rank Percentile (ARP)

Note: with large line-ups, ARP is practically equivalent to average AUC

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RP = 0.75

?next(RP = 0.25)

(RP = 0.50)

(RP = 1.0)Rank Percentile

properties: Ranges in (0,1] Higher is better Random scores ~0.5

in large lineups

Baselines

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Name Personalized?

Contextual?

General popularity No No

Sequential popularity No Yes

Temporal popularity No Yes

Device popularity* Yes No

LFM Yes No

LDA Yes No

* Only applicable to habitual recommendations

Contextual Personalized Recommenders

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SequentialLDA [LFM]: 3-way element-wise multiplication of device vector, context item and target item

TemporalLDA[LFM]: regular LDA/LFM score, multiplied by Temporal Popularity

TempSeqLDA[LFM]: 3-way score multiplied by Temporal Popularity

Results (1)Sequential Context Matters

Degradation when using a random item as context indicates that the correct context item reflects the current viewing session, and implicitly the current watchers of the device05/02/23 17

Results (2)Sequential Context Matters

Device Entropy: the entropy of p(topic | device) as computed by LDAon the training data; high values correspond to diverse distributions

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Results (3) - Exploratory Setting

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Conclusions Multi-user or shared devices pose challenging

recommendation problems. Sequential context helps – it “narrows" the topical variety

of the program to be watched next on the device. Intuitively, context serves to implicitly disambiguate

the current user or users of the device. 3-Way technique is an effective way of incorporating

sequential context that has no impact on learning.

Thank you! Questions?Please come visit the poster tomorrow.

Raz@yahoo-inc.com

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