Very basic introduction about Context-aware Recommendation.

1. Context-aware Recommendation:
A Quick View
Yong Zheng
Center for Web Intelligence
DePaul University, Chicago
Feb 23, 2016

2. Outline
• Background
Recommender Systems
Evaluation
Matrix Factorization
• Context-aware Recommendation
Context
Contextual PreFiltering
Contextual Modeling
• CARSKit: A Context-aware Recommendation Library
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3. Background

4. Recommender System (RS)
• RS: item recommendations tailored to user tastes
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5. How it works
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6. How it works
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7. How it works
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8. How it works
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Binary FeedbackRatings Reviews Behaviors
• User Preferences
Explicit Implicit

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Task and Eval: Rating Prediction
User Item Rating
U1 T1 4
U1 T2 3
U1 T3 3
U2 T2 4
U2 T3 5
U2 T4 5
U3 T4 4
U1 T4 3
U2 T1 2
U3 T1 3
U3 T2 3
U3 T3 4
Train
Test
Task: P(U, T) in testing set
Assume a simple model: P(U, T) = Avg (T)
P(U1, T4) = Avg(T4) = (5+4)/2 = 4.5
P(U2, T1) = Avg(T1) = 4/1 = 4
P(U3, T1) = Avg(T1) = 4/1 = 4
P(U3, T2) = Avg(T2) = (3+4)/2 = 3.5
P(U3, T3) = Avg(T3) = (3+5)/2 = 4
Mean Absolute Error (MAE) =
ei = R(U, T) – P(U, T)
MAE = (|3 – 4.5| + |2 - 4| + |3 - 4| +
|3 – 3.5| + |4 - 4|) / 5 = 1

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Task and Eval: Top-N Recommendation
User Item Rating
U1 T1 4
U1 T2 3
U1 T3 3
U2 T2 4
U2 T3 5
U2 T4 5
U3 T4 4
U1 T4 3
U2 T1 2
U3 T1 3
U3 T2 3
U3 T3 4
Train
Test
Task: Top-N Items to user U3
Assume a simple model: P(U, T) = Avg (T)
P(U3, T1) = Avg(T1) = 4/1 = 4
P(U3, T2) = Avg(T2) = (3+4)/2 = 3.5
P(U3, T3) = Avg(T3) = (3+5)/2 = 4
P(U3, T4) = Avg(T4) = (4+5)/2 = 3.5
Predicted Rank: T3, T1, T4, T2
Real Rank: T3, T2, T1
Precision@N = # of hits/N
Precision@1 = 1/1
Precision@2 = 2/2
Precision@3 = 2/3

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More Evaluation Metrics
• There are many more evaluation metrics
Task: Rating Prediction
MAE, RMSE, MSE, MPE, etc
Task: Top-N Recommendation
Relevance: Precision, Recall, F-Measure, AUC, etc
Ranking: MAP, NDCG, MRR, etc
Business Metrics
Retention rate, response rate, purchases, etc

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Matrix Factorization (MF)
User HarryPotter Batman Spiderman
U1 5 3 4
U2 ? 2 4
U3 4 2 ?
R P Q

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Matrix Factorization (MF)
R P Q
R = Rating Matrix, m users, n movies;
P = User Matrix, m users, f latent factors/features;
Q = Item Matrix, n movies, f latent factors/features;
Interpretation:
pu indicates how much user likes f latent factors;
qi means how much one item obtains f latent factors;
The dot product indicates how much user likes item;

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Matrix Factorization (MF)
minq,p S (u,i) e R ( rui - qt
i pu )2 + l (|qi|2 + |pu|2 )
Goal: Try to learn P and Q by minimizing the squared error
goodness of fit regularization
Goodness of fit: to reduce the prediction errors;
Regularization term: to alleviate the overfitting;

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Matrix Factorization (MF)
Optimization using stochastic gradient descent (SGD)
Parameter updates based on SGD

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MovieLens-100K, http://grouplens.org/datasets/movielens/
100K ratings given by 943 users on 1,682 movies
0.006
0.007
0.008
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0.01
0.011
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0.016
0.7
0.72
0.74
0.76
0.78
0.8
0.82
ItemAvg ItemKNN MF
Precision
MAE
MAE Precision@10
Matrix Factorization (MF)

17. Context-aware
Recommendation

18. Non-context vs Context
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Companion
• Decision Making = Rational + Contextual
• Examples:
 Travel destination: in winter vs in summer
 Movie watching: with children vs with partner
 Restaurant: quick lunch vs business dinner

19. What is Context?
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• “Context is any information that can be used to characterize
the situation of an entity” by Anind K. Dey, 2001
• Observed Context:
Contexts are those variables which may change when a same
activity is performed again and again.
• Examples:
Watching a movie: time, location, companion, etc
Listening to a music: time, location, emotions, occasions, etc

20. Context-aware RS (CARS)
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• Traditional RS: Users × Items Ratings
• Contextual RS: Users × Items × Contexts Ratings
Example of Multi-dimensional Context-aware Data set
User Item Rating Time Location Companion
U1 T1 3 Weekend Home Kids
U1 T2 5 Weekday Home Partner
U2 T2 2 Weekend Cinema Partner
U2 T3 3 Weekday Cinema Family
U1 T3 ? Weekend Cinema Kids

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• There are three ways to build algorithms for CARS
Context-aware RS (CARS)

22. Contextual PreFiltering

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• List of Contextual PreFiltering Algorithms
Reduction-based approach, 2005
Exact and Generalized PreFiltering, 2009
Item Splitting, 2009
User Splitting, 2011
Dimension as Virtual Items, 2011
User-Item Splitting, 2014
Contextual PreFiltering

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The underlying idea in item splitting is that the nature of an item, from the user's
point of view, may change in different contextual conditions, hence it may be
useful to consider it as two different items. (L. Baltrunas, F. Ricci, RecSys'09) – In
short, contexts are dependent with items.
Contextual PreFiltering (Item Splitting)
At Cinema At Home At Swimming Pool

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Contextual PreFiltering (Item Splitting)
User Item Location Rating
U1 M1 Pool 5
U2 M1 Pool 5
U3 M1 Pool 5
U1 M1 Home 2
U4 M1 Home 3
U2 M1 Home 2
High Rating
Low Rating
Significant difference?
Let’s split it !!!
M11: being seen at Pool
M12: being seen at Home
M1
Same movie,
different IDs.

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Contextual PreFiltering (Item Splitting)
User Item Loc Rating
U1 M1 Pool 5
U2 M1 Pool 5
U3 M1 Pool 5
U1 M1 Home 2
U4 M1 Home 3
U2 M1 Cinema 2
User Item Rating
U1 M11 5
U2 M11 5
U3 M11 5
U1 M12 2
U4 M12 3
U2 M12 2
Transformation
If there is qualified split,
one item will be split to
two new ones.
A binary contextual condition for
splitting:
“Pool” vs. “Non-Pool”
After transformation, we obtain a 2D User-Item rating matrix, so that any traditional
recommendation algorithms can be applied to.

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Contextual PreFiltering (Item Splitting)
User Item Loc Rating
U1 M1 Pool 5
U2 M1 Pool 5
U3 M1 Pool 5
U1 M1 Home 2
U4 M1 Home 3
U2 M1 Cinema 2
User Item Rating
U1 M11 5
U2 M11 5
U3 M11 5
U1 M12 2
U4 M12 3
U2 M12 2
Transformation
Question: How to find such a split? Pool and Non-pool, or Home and Non-home?
We employ a t-test on two pieces of ratings,
the best choice should help obtain the
largest t value and a small p-value (e.g., < 0.05)

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Contextual PreFiltering (Other Splitting)

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Example of Splitting Approaches
0.76
0.78
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0.9
0.92
0.94
0.96
MF ItemSplitting UserSplitting UISplitting
MAE
Restaurant data: 2309 ratings given by 50 users on 40 restaurants in context Time and Location

30. Contextual Modeling

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• List of Contextual Modeling Algorithms
 Tensor Factorization, 2010
 Factorization Machines, 2011
 Deviation-Based Context-aware Matrix Factorization, 2011
 Deviation-Based Contextual Sparse Linear Method, 2014
 Similarity-Based Context-aware Matrix Factorization, 2015
 Similarity-Based Contextual Sparse Linear Method, 2015
Contextual Modeling

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• Deviation-Based Context-aware MF (CAMF)
Contextual Rating Deviation (CRD): how user’s rating is deviated?
CRD(1) = 0.5  Users’ rating in Weekday is generally higher than
users’ rating at Weekend by 0.5
CRD(2) = -0.1  Users’ rating in Cinema is generally lower than
users’ rating at Home by 0.1
Deviation-Based Context-aware MF
Context D1: Time D2: Location
c1 Weekend Home
c2 Weekday Cinema
CRD(i) 0.5 -0.1

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Deviation-Based Context-aware MF: CAMF_C
Deviation-Based Context-aware MF
BiasedMF in Traditional RS:
CAMF_C Approach:
Global Average Rating User bias Item Bias User-Item interaction
Contextual Rating Deviation

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Deviation-Based Context-aware MF: CAMF_CU & CAMF_CI
Deviation-Based Context-aware MF
BiasedMF in Traditional RS:
CAMF_C Approach:
Global Average Rating User bias Item Bias User-Item interaction
CAMF_CU Approach:
CAMF_CI Approach:

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Example: CAMF
Restaurant data: 2309 ratings given by 50 users on 40 restaurants in context Time and Location
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
MF ItemSplitting UserSplitting UISplitting CAMF_C CAMF_CI CAMF_CU
MAE

36. CARSKit: A Library

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CARSKit: A Java-based Open-source
Context-aware Recommendation Library
There are many recommendation library for traditional recommendation.
Users × Items Ratings

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CARSKit: A Java-based Open-source
Context-aware Recommendation Library
CARSKit: https://github.com/irecsys/CARSKit
Users × Items × Contexts Ratings

39. Yong Zheng
Center for Web Intelligence
DePaul University, Chicago
Feb 23, 2016
Context-aware Recommendation:
A Quick View