Context-Aware Recommender Systems (CARSs) suffer from the cold-start problem, i.e., the inability to provide accurate recommendations for new users, items or contextual situations. In this research, we aim at solving this problem by exploiting various hybridisation techniques, from simple heuristic-based solutions to complex adaptive solutions, in order to take advantage of the strengths of different CARS algorithms while avoiding their weaknesses in a given (cold-start) situation. Our initial research based on offline experiments using various contextually-tagged rating datasets has shown that basic CARS algorithms perform very differently in different recommendation scenarios, and that they can be effectively hybridised to achieve an overall optimal performance. Further research is now required to find the optimal method for hybridisation.

1. Hybridisation Techniques for Cold-Starting
Context-Aware Recommender Systems
Matthias Braunhofer
!
Free University of Bozen - Bolzano
Piazza Domenicani 3, 39100 Bolzano, Italy
mbraunhofer@unibz.it
RecSys - October 2014, Foster City, USA

2. RecSys - October 2014, Foster City, USA
Outline
2
• Context-Aware Recommenders and the Cold-Start Problem
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues

3. RecSys - October 2014, Foster City, USA
Outline
2
• Context-Aware Recommenders and the Cold-Start Problem
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues

4. Context-Aware Recommender Systems
• Context-Aware Recommender Systems (CARSs) aim to provide better
recommendations by exploiting contextual information (e.g., weather)
• Rating prediction function is: R: Users x Items x Context → Ratings
RecSys - October 2014, Foster City, USA
3
3 ? 4
2 5 4
? 3 4
1 ? 1
2 5
? 3
3 ? 5
2 5
? 3
5 ? 5
4 5 4
? 3 5

5. Example: Google Now
• “The right information at just the right time”
RecSys - October 2014, Foster City, USA
4
Nearby photo spots Traffic & transit Nearby attractions

6. Example: South Tyrol Suggests (STS)
• Our Android app that offers context-aware place of interest (POI)
recommendations for the South Tyrol region of Italy
Personality questionnaire Rating screen Suggestions screen
RecSys - October 2014, Foster City, USA
5

7. Cold-Start Problem
• CARSs suffer from the cold-start problem
• New user problem: How do you recommend to a new user?
• New item problem: How do you recommend a new item with no ratings?
• New context problem: How do you recommend in a new context?
RecSys - October 2014, Foster City, USA
6
1 ? 1 ?
2 5 ?
? 3 ?
3 ? 5 ?
2 5 ?
? 3 ?
5 ? 5 ?
4 5 4 ?
? 3 5 ?
1 ? 1
2 5
? 3
3 ? 5
2 5
? 3
5 ? 5
4 5 4
? 3 5
? ? ?
? ? ?
1 ? 1
2 5
? 3
3 ? 5
2 5
? 3
5 ? 5
4 5 4
? 3 5

8. Our Solution: Hybrid CARS
• Intuition: it is possible to adaptively combine multiple CARS algorithms in
order to take advantage of their strengths and alleviate their drawbacks when
predicting a user’s rating for an item given a particular cold-start situation
• Example:
RecSys - October 2014, Foster City, USA
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(user, item,
context) tuple
CARS 1
CARS 2
Combination Final score
Score
Score
Hybrid CARS

9. • Context-Aware Recommenders and the Cold-Start Problem
RecSys - October 2014, Foster City, USA
Outline
8
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues

10. RecSys - October 2014, Foster City, USA
Related Work
9
Cold-starting CARSs
… using additional data … better processing known
data
Active Learning
(Elahi et al., 2013)
Cross-domain recs.
(Enrich et al., 2013)
Implicit feedback
(Shi et al., 2012)
User / item attributes
(Woerndl et al., 2009)
Context similarities
(Codina et al., 2013)
Survey data
(Baltrunas et al., 2012)

11. RecSys - October 2014, Foster City, USA
Related Work
9
Cold-starting CARSs
… using additional data … better processing known
data
Active Learning
(Elahi et al., 2013)
Cross-domain recs.
(Enrich et al., 2013)
Implicit feedback
(Shi et al., 2012)
User / item attributes
(Woerndl et al., 2009)
Context similarities
(Codina et al., 2013)
Survey data
(Baltrunas et al., 2012)
No unique optimal
solution!

12. • Context-Aware Recommenders and the Cold-Start Problem
RecSys - October 2014, Foster City, USA
Outline
10
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues

13. MF Methods
• Matrix Factorisation (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; features associated
with the user should match with the features associated with the item
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
RecSys - October 2014, Foster City, USA
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
= z
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpu

14. MF Methods
• Matrix Factorisation (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; features associated
with the user should match with the features associated with the item
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
RecSys - October 2014, Foster City, USA
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
= z
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
Rating prediction ȓui = qiTpu

15. MF Methods
• Matrix Factorisation (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; features associated
with the user should match with the features associated with the item
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
Item preference factor
RecSys - October 2014, Foster City, USA
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
= z
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpu
vector

16. MF Methods
• Matrix Factorisation (MF) predicts unknown ratings by discovering some
latent features that determine how a user rates an item; features associated
with the user should match with the features associated with the item
r q p
5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
RecSys - October 2014, Foster City, USA
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
x
y
= z
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpu User preference factor
vector

17. Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
• CAMF-CC (Context-Aware Matrix Factorisation for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
kΣ
Σ
RecSys - October 2014, Foster City, USA
12
ˆ ruic1,...,ck = qi
T pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

18. Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
• CAMF-CC (Context-Aware Matrix Factorisation for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
kΣ
Σ
RecSys - October 2014, Foster City, USA
12
ˆ ruic1,...,ck = qi
T pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

19. Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
• CAMF-CC (Context-Aware Matrix Factorisation for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
kΣ
Σ
RecSys - October 2014, Foster City, USA
12
ˆ ruic1,...,ck = qi
T pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

20. Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
• CAMF-CC (Context-Aware Matrix Factorisation for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
kΣ
Σ
RecSys - October 2014, Foster City, USA
12
ˆ ruic1,...,ck = qi
T pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

21. Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
• CAMF-CC (Context-Aware Matrix Factorisation for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
kΣ
Σ
RecSys - October 2014, Foster City, USA
12
ˆ ruic1,...,ck = qi
T pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

22. Basic CARS Algorithms
CAMF-CC (Baltrunas et al., 2011)
• CAMF-CC (Context-Aware Matrix Factorisation for item categories) is a
variant of CAMF that extends standard MF by incorporating baseline
parameters for contextual condition-item category pairs
kΣ
Σ
RecSys - October 2014, Foster City, USA
12
ˆ ruic1,...,ck = qi
T pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

23. Basic CARS Algorithms
SPF (Codina et al., 2013)
• SPF (Semantic Pre-Filtering) is a contextual pre-filtering method that, given
a target contextual situation, uses a standard MF model learnt from all the
ratings tagged with contextual situations identical or similar to the target one
• Conjecture: addresses cold-start problems caused by exact pre-filtering
• Key step: similarity calculation
RecSys - October 2014, Foster City, USA
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1 -0.5 2 1
-2 0.5 -2 -1.5
-2 0.5 -1 -1
1 -0.96 -0.84
-0.96 1 0.96
-0.84 0.96 1
Condition-to-item co-occurrence matrix Cosine similarity between conditions

24. Basic CARS Algorithms
Content-based CAMF-CC
• It is a novel variant of CAMF-CC that incorporates additional sources of
information about the items, e.g., category or genre information
• Conjecture: alleviates the new item problem of CAMF-CC
kΣ
Σ
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Σ T
ˆ ruic1,...,ck = (qi + xa )
a∈A(i )
pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
A(i) set of item attributes
xa latent factor vector of item attribute a
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

25. Basic CARS Algorithms
Content-based CAMF-CC
• It is a novel variant of CAMF-CC that incorporates additional sources of
information about the items, e.g., category or genre information
• Conjecture: alleviates the new item problem of CAMF-CC
kΣ
Σ
RecSys - October 2014, Foster City, USA
14
Σ T
ˆ ruic1,...,ck = (qi + xa )
a∈A(i )
pu +μ + bi + bu + btcj
j=1
t∈T (i )
qi latent factor vector of item i
A(i) set of item attributes
xa latent factor vector of item attribute a
pu latent factor vector of user u
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

26. Basic CARS Algorithms
Demographics-based CAMF-CC
• It is a novel variant of CAMF-CC that profiles users through known user
attributes (e.g., age group, gender, personality traits)
• Conjecture: alleviates the new user problem of CAMF-CC
kΣ
Σ +μ + b+ b+ Σ
bi u tcj
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ˆ ruic1,...,ck = qi
T (pu + ya )
a∈A(u)
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user attributes
ya latent factor vector of user attribute a
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

27. Basic CARS Algorithms
Demographics-based CAMF-CC
• It is a novel variant of CAMF-CC that profiles users through known user
attributes (e.g., age group, gender, personality traits)
• Conjecture: alleviates the new user problem of CAMF-CC
kΣ
Σ +μ + b+ b+ Σ
bi u tcj
RecSys - October 2014, Foster City, USA
15
ˆ ruic1,...,ck = qi
T (pu + ya )
a∈A(u)
j=1
t∈T (i )
qi latent factor vector of item i
pu latent factor vector of user u
A(u) set of user attributes
ya latent factor vector of user attribute a
μ overall average rating
bi baseline for item i
bu baseline for user u
T(i) set of categories associated to item i
btcj baseline for item category-contextual condition tcj

28. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
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(user, item, context)
tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
Y
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score

29. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
Y
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple

30. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
Y
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple

31. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
Y
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple

32. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
Y
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple
Y

33. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
Y
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple
Y

34. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
N
New
context?
Y
N
New
item?
New
user?
Content-CAMF-CC &
Demogr.-CAMF-CC
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple
Y
Y

35. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
N
New
context?
Y
N
New
item?
New
user?
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple
Y
Y
Content-CAMF-CC &
Demogr.-CAMF-CC

36. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Final score
Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
N
New
context?
Y
N
New
item?
New
user?
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
new user, new item,
known context) tuple
Y
Y
Content-CAMF-CC &
Demogr.-CAMF-CC
Score

37. Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a stable heuristic to switch between the basic
CARS algorithms depending on the encountered cold-start situation
• Conjecture: better tackles all kinds of cold-start situations found in CARSs
New
context?
RecSys - October 2014, Foster City, USA
16
(user, item, context)
tuple
Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New
item?
N
N
New
context?
Y
N
New
item?
New
user?
Y
N
Y
N
Final score
Final score
Final score
Score
Score
Score
Score
new user, new item,
known context) tuple
Y
Y
Content-CAMF-CC &
Demogr.-CAMF-CC
Final score

38. Hybrid CARS Algorithms
Adaptive Weighted (1/2)
• Adaptive Weighted adaptively weights each basic CARS algorithm based on
its predicted accuracy for the user, item and contextual situation in question
• Extends the two-dimensional adaptive RS presented in (Bjørkøy, 2011)
• Conjecture: optimises adaptation of differently performing CARS algorithms
Score
Error
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(user, item,
context) tuple
CAMF-CC
Weighted score Final score
Error model
SPF
Error model
Content-CAMF-CC
Error model
Demogr.-CAMF-Error
model
Score
Error
Score
Error
Score
Error

39. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
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ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

40. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

41. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

42. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

43. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

44. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

45. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

46. Hybrid CARS Algorithms
Adaptive Weighted (2/2)
• Builds for each basic CARS algorithm a new user-item-context error tensor
whose entries are the known deviations (errors) of the CARS predictions from
the true ratings
• Uses a separate CARS error prediction model for each of these error tensors
to predict the errors (accuracies) on a particular (user, item, context) tuple
Σ )T (pu + ycu
Σ )+μ + bi + bu
RecSys - October 2014, Foster City, USA
18
ˆeuic1,...,ck = (qi + xci
ci∈IC
cu∈UC
qi latent factor vector of item i
pu latent factor vector of user u
IC subset of item-related contextual conditions
xci latent factor vector of contextual condition ci
UC subset of user-related contextual conditions
ycu latent factor vector of contextual condition cu
μ overall average error
bi baseline for item i
bu baseline for user u

47. RecSys - October 2014, Foster City, USA
Outline
19
• Context-Aware Recommenders and the Cold-Start Problem
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues

48. RecSys - October 2014, Foster City, USA
Evaluation
Used Datasets
• 3 contextually-tagged rating datasets
20
STS
(Braunhofer et al., 2013)
LDOS-CoMoDa
(Odić et al., 2013)
Music
(Baltrunas et al., 2011)
Domain POIs Movies Music
Rating scale 1-5 1-5 1-5
Ratings 2,534 2,296 4,012
Users 325 121 43
Items 249 1,232 139
Contextual factors 14 12 8
Contextual conditions 57 49 26
Contextual situations 931 1,969 26
User attributes 7 4 10
Item features 1 7 2

49. RecSys - October 2014, Foster City, USA
Evaluation
Evaluation Procedure
• Randomly divide the entities (i.e., users, items or contexts) into ten cross-validation
folds
• For each fold k = 1, 2, …, 10
• Use all the ratings except those coming from entities in fold k as training
set to build the prediction models
• Calculate the Mean Absolute Error (MAE) and normalised Discounted
Cumulative Gain (nDCG) on the test ratings for the entities in fold k
• Advantage: allows to test the models on really cold entities
• Disadvantage: can’t test for different degrees of coldness
21

50. Results
Recommendation for New Users
1-nDCG@1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
RecSys - October 2014, Foster City, USA
22
MAE
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
STS CoMoDa Music
STS CoMoDa Music
CAMF-CC SPF Content-based CAMF-CC
Demographics-based CAMF-CC Average Weighted Heuristic Switching
Adaptive Weighted

51. Results
Recommendation for New Items
1-nDCG@1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
RecSys - October 2014, Foster City, USA
23
MAE
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.6
0.5
0.4
0.3
0.2
0.1
0.0
STS CoMoDa Music
STS CoMoDa Music
CAMF-CC SPF Content-based CAMF-CC
Demographics-based CAMF-CC Average Weighted Heuristic Switching
Adaptive Weighted

52. Results
Recommendation under New Contexts
1-nDCG@1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
RecSys - October 2014, Foster City, USA
24
MAE
1.2
1.1
1.0
0.9
0.8
0.7
0.5
0.4
0.3
0.2
0.1
0.0
STS CoMoDa Music
STS CoMoDa Music
CAMF-CC SPF Content-based CAMF-CC
Demographics-based CAMF-CC Average Weighted Heuristic Switching
Adaptive Weighted

53. RecSys - October 2014, Foster City, USA
Outline
25
• Context-Aware Recommenders and the Cold-Start Problem
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues

54. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
RecSys - October 2014, Foster City, USA
Conclusions
26
likes
SKIING
FREERIDING
ALPING
SKIING
likes
MUSEUM
MUSEUM
likes

55. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
FREERIDING
ALPING
SKIING
likes
MUSEUM
MUSEUM
likes

56. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
FREERIDING
ALPING
SKIING
likes
MUSEUM
MUSEUM
likes

57. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
MUSEUM
MUSEUM
likes

58. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
Skiing
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
Skiing
MUSEUM
MUSEUM
likes

59. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
Skiing
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
similar
Skiing
MUSEUM
MUSEUM
likes

60. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
Skiing
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
likely likes similar
Skiing
MUSEUM
MUSEUM
likes

61. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
Skiing
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
likely likes similar
Skiing
likes Wet
MUSEUM
MUSEUM
weather
Wet
weather

62. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
Skiing
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
likely likes similar
Skiing
MUSEUM
MUSEUM
likes
similar
Wet
weather
Wet
weather

63. • Various cold-start situations require different CARS solutions
• Hybridisation of several CARS techniques, each of which has its own
strengths and weaknesses, allows to achieve best (cold-start) performance
• First developed and tested hybrid CARS algorithms are able to outperform
the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)
Skiing
RecSys - October 2014, Foster City, USA
Conclusions
26
SKIING
18-25
Male
18-25
Male
likes
similar
likely likes
FREERIDING
ALPING
SKIING
likes
likely likes similar
Skiing
MUSEUM
MUSEUM
likes
likely likes similar
Wet
weather
Wet
weather

64. RecSys - October 2014, Foster City, USA
Open Issues
• Review additional knowledge sources which may be used to incorporate
additional information about users, items and contextual situations
• Check the availability of large-scale, contextually-tagged datasets with item
and user attributes
• Revise the used evaluation procedure and evaluation metrics
• Identify the best-performing hybridisation method for cold-start situations
• Design and execute a live user study
27

65. RecSys - October 2014, Foster City, USA
Questions?
Thank you.