Collaborative Nowcasting for Contextual Recommendation

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1 Collaborative for Contextual Recommendation Yu Sun 1, Nicholas Jing Yuan 2, Xing Xie 3, Kieran McDonald 4, Rui Zhang 5 University of Melbourne { 1 sun.y, 5 rui.zhang}@unimelb.edu.au Microsoft Research Microsoft Corporation { 2 nicholas.yuan, 3 xing.xie, 4 kieran.mcdonald}@microsoft.com April 14 th 2016

2 Outline 1 Motivation and Problem Definition 2 3 4

3 Outline Motivation Problem Definition 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

4 Motivation Problem Definition Proactive Experiences on Mobile Phones Digital assistants: Cortana, Google Now, Siri. Proactive experiences recommend the right information at just the right time a and help you get things done b even before you ask c. a b c

5 Motivation Problem Definition Proactive Experiences on Mobile Phones Information types: videos, news, traffic, weather, apps, places, (calendar, stock prices, sports) etc. Each type in such a layout is called a card

6 Motivation Problem Definition Proactive Experiences on Mobile Phones Limited display size = show one or two cards Which card a user needs = intent to have dinner = restaurant card to drive home = traffic card To recommend the right information at the right time, need monitor intent

7 Intent and Context Motivation Problem Definition Intent Context external context: physical environment (e.g., time, location) internal context: users states (e.g., activity, usage of apps) Example (From Context to Intent) context: 6:00 p.m., in the office intent: to drive home context: just left a shopping center, using Yelp intent: to find a restaurant

8 Intent and Context Motivation Problem Definition Relationship between context and intent is difficult to model intent and context change swiftly exhibit strong sequential correlation Context itself is heterogeneous and complicated all contemporaneous information related to the intent Challenge to model structure of context relationship between context and intent

9 Existing Work Motivation Problem Definition Traditional recommendation models cannot tackle the challenge only deal with a given intent (e.g., to find movies, books) recommend new items fulfilling the given intent Time-aware recommendation models also cannot apply do not consider other context besides time not suitable for swiftly changing context/intent Context-aware recommendation models do not work either do not take sequential correlation into account consider only external context (e.g., time, location)

10 Motivation Problem Definition New Recommendation Paradigm Personal Digital Assistant-Style Recommendation user-centered rather than product/item-centered recommendation based on multiple types of intent First step: monitoring real-time intent by context Wide Applications Proactive experiences Online advertisement

11 Outline Motivation Problem Definition 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

12 Intent Monitoring Problem Motivation Problem Definition The studied problem is defined as follows: Definition (Intent Monitoring) Given a starting time t 0, a monitoring granularity, a type of intent γ and the context Xt u of user u, the intent monitoring problem is to predict whether user u has intent γ with context Xt u for each time step t of length starting from t 0. Example Time step 10 a.m. 11 a.m 12 p.m. 1 p.m. Now News intent ?

13 Outline 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

14 Why? UW Engineering Bldg: Waterproofing went on one day 1 1 Picture from: Cliff Mass, University of Washington

15 Why? Washed off a few hours later 1 1 Picture from: Cliff Mass, University of Washington

16 Why? Reapplied the next day. How much did this cost? 1 1 Picture from: Cliff Mass, University of Washington

17 Difference between Nowcast and Forecast historical data side data historical data variable of interest variable of interest (a) Nowcast (b) Forecast Nowcast: prediction of current or very near future Difference: side data contemporaneous with more frequently available (e.g., industrial output GDP)

18 Side-Data Used in In meteorology: nowcasting weather atmospheric conditions from aircraft water vapor distributions from GPS receivers social media data from Facebook, Twitter, etc. In macroeconomics: nowcasting GDP personal consumption, industrial production surveys, financial variables (e.g., interest rates, CPI) Google trend data In data mining: nowcasting rainfall, illness rates search engine query log (e.g., Google trend) posts in social media (e.g., Twitter)

19 Existing Model Cannot Apply Thunderstorm: linear regression with exponential smoothing variable of interest quite different from intent GDP nowcasting: dynamic factor model granularity much larger than hours macroeconomic variables are non-personalized Rainfall nowcasting: Bootstrapped LASSO + regression cannot address the personalized scenario hard to obtain textual features for personalized intent

20 Outline 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

21 The Panel Context stochastic processes Historical/side data time series All series for user u panel X u Time Step 10 a.m. 11 a.m 12 p.m. 1 p.m. Now Facebook Skype McDonald s IKEA Dist-to-Office Day-of-Week News Intent ?

22 Latent Factor Structure Latent Factors We assume that x i,t X has structure x i,t = λ i f t +ξ i,t, where f t = (f 1,t,.., f R,t ), λ i = (λ i,1,..,λ i,r ), and ξ i,t N(0, ψ 2 i,t ). Written in matrix form x t = Λf t +ξ t where x t = (x 1,t,.., x N,t ), Λ = (λ 1,..,λ N ), ξ t = (ξ 1,t,..,ξ N,t ) Factor Transition To exploit sequential pattern, we assume dynamics of latent factors have structure f t = Af t 1 + Bω t where A R R R, B R R Q, and ω t WN(0, I Q ).

23 Outline 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

24 Estimation Overview User panel X u X Loading ˆΛ u F Collaborative latent factors For each u ˆΛ u F X u For each u ˆF u Personalized latent factors Intent s u Extracting Collaborative Latent Factors Kalman Filtering Regression Figure:

25 Collaborative Latent Factors: Using CP Decomposition M T w 1 w r N X v v r u 1 u r Estimation of Parameters After CP decomposition X u UD (u) V where D (u) = diag(w u,1,..., W u,r ), and U R N R, V R T R, W R M R. We have F = V and ˆΛ u = U u D (u).

26 Using PARAFAC2 Decomposition X u G u V L u Estimation of Parameters After PARAFAC2 decomposition X u G u HL u V where G u R Nu R, H R R R is invariant to u, L u R R R. We have F = V and ˆΛ u = G u HL u.

27 Personalized Latent Factors Time Update (Prediction) Collaborative latent factors are not sufficient static common structure unsuitable for personalized scenario Apply Kalman Filter on F u and X u ft = Âˆf t 1 + ˆBω t P t = ÂˆP t 1 Â + ˆΨ t Measurement Update (Correction) K t = P t ˆΛ (ˆΛ P t ˆΛ + ˆΨ t ) 1 ˆft = f t + K t (x t ˆΛ f t ) ˆP t = (I K t ˆΛ) P t

28 Outline Set-up Results 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

29 Data Sets Set-up Results Proactive log of a commercial digital assistant from June 10 th 2015 to July 9 th 2015 clicks as indicators of intent eight types of intent: news, weather, etc. in total contain 20,807 anonymous users Collect intent-related context, apps used and venues visited

30 Set-up Results Measurements and Compared Methods Measurements Macro F-measure: average performance among all users Micro F-measure: performance per instance Compared Methods BoostedTree: used in existing contextual ranking models FM: (factorization machine) for next-basket recommendation NowcastIndi: the macroeconomic nowcasting model CNowcastCP: CP decomp. for collaborative latent factors

31 Outline Set-up Results 1 Motivation and Problem Definition Motivation Problem Definition 2 3 Set-up Results 4

32 1 R 1 Q Motivation and Problem Definition Set-up Results Effect of Parameters R and Q 1.1 News Weather Finance Sports 1.1 News Weather Finance Sports RelativeMacroF-measure RelativeMacroF-measure Observation R = 4, Q = 2 is a good choice small performance variance

33 Set-up Results Comparison across Models Macro F-measure Model News Events Weather Places Finance Calendar Traffic Sports BoostedTree FM NowcastIndi CNowcastCP CNowcast Micro F-measure Model News Events Weather Places Finance Calendar Traffic Sports BoostedTree FM NowcastIndi CNowcastCP CNowcast

34 0 0 Motivation and Problem Definition Set-up Results Comparison across Monitoring Granularity PerformanceRatio MacroF-measure MicroF-measure MonitoringGranularity (h) (a) Ratio to BoostedTree PerformanceRatio MacroF-measure MicroF-measure MonitoringGranularity (h) (b) Ratio to FM Observation monitoring granularity ր performance advantage ր closer to now" suitable for nowcasting

35 Conclusion Contextual Intent Monitoring users real-time intent with context is key for personal digital assistant-style recommendation. The collaborative nowcasting model effectively models the complicated relationship between context and intent via nowcasting and collaborative capabilities.

Contextual Intent Tracking for Personal Assistants

Contextual Intent Tracking for Personal Assistants Yu Sun 1, Nicholas Jing Yuan, Yingzi Wang 3, Xing Xie 4, Kieran McDonald 5, Rui Zhang 6 Department of Computing and Information Systems, University of