Predicting Viewer Subscriptions in Cable TV Services. Bing Tian Dai and Hady W. Lauw

Transcription

1 Predicting Viewer Subscriptions in Cable TV Services Bing Tian Dai and Hady W. Lauw

2 2 TV Competitions Conventional TV Broadcasting Cable TV Internet TV Web TV IP TV Live Television Time Shifted Television Video on Demand

3 TV Competitions 3

4 What shall Cable TV Provider do? Increase prices? Increase advertisement? Upselling? Yes! attract existing customers to subscribe more provide add-on services to existing customers

5 How to Upsell? What does Internet TV do? Time Shifted TV Watch when I want VOD Watch what I want Customization! Cable TV Upselling Understanding their existing customers Live Television Time Shifted Television Video on Demand Recommend them the right channel or package

6 Challenges in Understanding Customers Are all channels available to a customer? Availability Constraints Can I subscribe whichever channel a customer wants? Bundle Constraints

7 Availability Constraints Availability constraints are constraints imposed on users to indicate which items are available to each user A user can only watch the available cable TV channels, i.e., those that she has subscribed to We can only observe consumption behaviors from available channels, but not from unavailable channels

8 Bundle Constraints Channels HBO, FOX Movies Packs HBO Pak, FOX Movie Pack Bundles Kids, Education, Entertainment

9 Availability & Bundle Constraints Kat only subscribes to bundle A Linda subscribes to both A and B

10 Availability & Bundle Constraints We cannot observe Kat s interests on bundle B, which does not mean that Kat does not like the channels in B Linda does not like channels in B, since she subscribes but never watches

11 Availability & Bundle Constraints Can we say Kat is more similar to Maggie than to Linda?

12 Modeling Customers Preferences Channels Words Preferences Topics Customers Articles Modeling Customers Preferences Topic Model

13 Modeling Customers Preferences Latent Dirichlet Allocation (LDA) does not consider availability constraints Latent Transition Model (LTM) considers availability constraints

14 Latent Dirichlet Allocation Preference Prior Channel Prior Preference prior first generates a topical preference θ n for user n Channel prior generates a channel distribution β t for each topic t

15 Latent Dirichlet Allocation Preference Prior Channel Prior User n chooses a topic t according to her preference θ n She then chooses channel w according to the probability distribution β t

16 LDA: an Example Preference Prior Channel Prior Ivy s preference is {<movie,0.6>, <ent,0.3>, <edu,0.1>} movie: <ch601,0.3>, <ch602,0.2>, <ch605,0.2> ent: <ch402,0.2>, <ch401,0.15>, <ch601,0.1>

17 Latent Transition Model Partially Observed Channels Transition Prior Topic Transition c 2 is observed when c 1 is not available If c 1 is not available, she may transition from topic t 1 to t 2 She chooses c 2 from t 2, we observe that she watches c 2

18 Latent Transition Model Partially Observed Channels Transition Prior Topic Transition c 2 is observed when c 1 is not available Movie ch602 is not available to Ivy Ivy then chooses an education ch102 instead

19 Dataset Description 7 Bundles Choose Customers with at least 4 bundles subscription Randomly choose one bundle to hide from each customer Only observe the watching history from the remaining bundles Predict the next bundle the customer may subscribe to, and compare it with the hidden bundle

20 Baseline Methods MaxComb Choose the bundle that give the most number of customers after combining with the existing bundles Ivy subscribes to bundles 1, 2, 3, now choose one among 4, 5, 6, 7, consider {1,2,3,4}, {1,2,3,5}, {1,2,3,6} and {1,2,3,7}, see which one gives the most number of subscriptions LDA

21 Bundle Prediction

22 Topic Transitions (I) Chinese language channels are grouped together under one topic Kids channels are spit into 2 topics, one for toddlers and younger kids, the other for elder kids and teenagers: transitions happen from other topics to the second topic, but not to the first topic

23 Topic Transitions (II) There are also 2 topics for education channels, one with more serious channel and narrower range of audiences, e.g., Crime & Investigation Network: transitions happen from harder to understand channels to easier ones, but not the other way around

24 Topic Transitions (III) Transitions does not happen from HD channels to same category channels with normal resolution: People without HD subscription do not care if they are watching HD or normal resolution channels

25 Can LTM explain all Transitions? LTM assumes the channels before and after topic transitions are watched by the same viewer When the viewer changes, the topic transitions are not expected to follow the patterns discovered by LTM

26 Conclusion LTM models users preferences with availability constraints LTM can be combined with pricing analysis for channel prediction LTM can be applied to solve viewer identification problem

27 THANK YOU! QUESTIONS?