This document describes a contextual TV recommendation system called Watch-It-Next that provides recommendations for shared smart TV devices. It uses the program currently being watched as contextual information to infer who is likely watching and narrow the recommendations. It presents a technique called "3-Way" that learns a standard matrix factorization model and scores recommendation candidates based on their agreement with both the device and context item vectors, without changing the learning algorithm. An evaluation on a large dataset of TV viewing data found the contextual recommendations outperformed non-contextual baselines, indicating the current context helps disambiguate who is watching on shared devices.

1. Watch-It-Next:
A Contextual TV Recommendation System
Michal Aharon, Eshcar Hillel, Amit Kagian, Ronny Lempel, Hayim Makabee, Raz
Nissim

2. Recommendation in Personal
Devices and Accounts
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3. 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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4. 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.

5. Solution: Using Context
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 Previous work: time of day

6. Context in this Work:
Current Item Being Watched
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7. 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

8. WatchItNext Inputs and Output
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Available programs,
a.k.a. “line-up”
Ranked
recommendations

9.  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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10.  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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11.  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|
V
W
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12. 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..kvd(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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13. 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*
Triplets
339647 17232 More than 19M
Setting Test Instances Average Line-up Size
Habitual ~3.8M 390
Exploratory ~1.7M 349

14. Metric: Avg. Rank Percentile (ARP)
Note: with large line-ups, ARP is practically equivalent to average
AUC09/10/15 14
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

15. 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

16. 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

17. 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 device
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18. Results (2)
Sequential Context Matters
Device Entropy: the entropy of p(topic | device) as computed by LDA
on the training data; high values correspond to diverse distributions
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19. Results (3) - Exploratory Setting
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20. 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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