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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.
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RecSys - October 2014, Foster City, USA
Hybridisation Techniques for Cold-Starting Context-Aware Recommender Systems
Matthias Braunhofer!
Free University of Bozen - BolzanoPiazza Domenicani 3, 39100 Bolzano, Italy
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
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
RecSys - October 2014, Foster City, USA
• 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
Context-Aware Recommender Systems
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
RecSys - October 2014, Foster City, USA
Example: Google Now
• “The right information at just the right time”
4
Nearby photo spots Traffic & transit Nearby attractions
RecSys - October 2014, Foster City, USA
Example: South Tyrol Suggests (STS)
• Our Android app that offers context-aware place of interest (POI) recommendations for the South Tyrol region of ItalyPersonality questionnaire Rating screen Suggestions screen
5
RecSys - October 2014, Foster City, USA
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?
6
1 ? 1 ?
2 5 ?
? 3 ?
3 ? 5 ?
2 5 ?
? 3 ?
5 ? 5 ?
4 5 4 ?
? 3 5 ?
1 ? 12 5? 3
3 ? 52 5? 3
5 ? 54 5 4? 3 5? ? ?
? ? ?1 ? 1
2 5
? 3
3 ? 5
2 5
? 3
5 ? 5
4 5 4
? 3 5
RecSys - October 2014, Foster City, USA
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:
7
(user, item, context) tuple
CARS 1
CARS 2
Combination Final score
Score
Score
Hybrid CARS
RecSys - October 2014, Foster City, USA
Outline
8
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues
• Context-Aware Recommenders and the Cold-Start Problem
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)
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!
RecSys - October 2014, Foster City, USA
Outline
10
• Related Work
• Context-Aware Rating Prediction Models
• Evaluation and Results
• Conclusions and Open Issues
• Context-Aware Recommenders and the Cold-Start Problem
RecSys - October 2014, Foster City, USA
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
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
xyz=
r q p5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpu
RecSys - October 2014, Foster City, USA
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
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
xyz=
r q p5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpuRating prediction
RecSys - October 2014, Foster City, USA
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
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
xyz=
r q p5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpu
Item preference factor vector
RecSys - October 2014, Foster City, USA
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
11
r11 r12 r13 r14
r21 r22 r23 r24
r31 r32 r33 r34
r41 r42 r43 r44
r51 r52 r53 r54
a b c
xyz=
r q p5 x 4 matrix 5 x 3 matrix 3 x 4 matrix
r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z
ȓui = qiTpu User preference factor vector
RecSys - October 2014, Foster City, USA
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
12
ruic1,...,ck = qiT pu + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
12
ruic1,...,ck = qiT pu + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
12
ruic1,...,ck = qiT pu + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
12
ruic1,...,ck = qiT pu + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
12
ruic1,...,ck = qiT pu + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
12
ruic1,...,ck = qiT pu + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
13
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
RecSys - October 2014, Foster City, USA
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
14
ruic1,...,ck = (qi + xa )a∈A(i )∑ T
pu + µ + bi + bu + btcjj=1
k
∑t∈T (i )∑
qi latent factor vector of item iA(i) set of item attributes xa latent factor vector of item attribute apu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
14
ruic1,...,ck = (qi + xa )a∈A(i )∑ T
pu + µ + bi + bu + btcjj=1
k
∑t∈T (i )∑
qi latent factor vector of item iA(i) set of item attributes xa latent factor vector of item attribute apu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
15
ruic1,...,ck = qiT (pu + ya )
a∈A(u )∑ + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uA(u) set of user attributesya latent factor vector of user attribute aμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
15
ruic1,...,ck = qiT (pu + ya )
a∈A(u )∑ + µ + bi + bu + btcj
j=1
k
∑t∈T (i )∑
qi latent factor vector of item ipu latent factor vector of user uA(u) set of user attributesya latent factor vector of user attribute aμ overall average ratingbi baseline for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
Y
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
Y
New item?
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
Y
New item?
Y
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
Y
New item?
Y Content-CAMF-CC & Demogr.-CAMF-CC
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
Y
New item?
Y Content-CAMF-CC & Demogr.-CAMF-CC
Score
RecSys - October 2014, Foster City, USA
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
16
(user, item, context) tuple
Final score
Y Demogr.-CAMF-CC
Content-CAMF-CC
CAMF-CC
New item?
N
Y
N
New context?
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
New user?
Y
New item?
Y Content-CAMF-CC & Demogr.-CAMF-CC Final score
Score
RecSys - October 2014, Foster City, USA
• 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
Hybrid CARS Algorithms Adaptive Weighted (1/2)
17
(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
Score
Error
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
RecSys - October 2014, Foster City, USA
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
18
euic1,...,ck = (qi + xcici∈IC∑ )T (pu + ycu
cu∈UC∑ )+ µ + bi + bu
qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cuμ overall average errorbi baseline for item ibu baseline for user u
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
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 MusicRating scale 1-5 1-5 1-5Ratings 2,534 2,296 4,012Users 325 121 43Items 249 1,232 139Contextual factors 14 12 8Contextual conditions 57 49 26Contextual situations 931 1,969 26User attributes 7 4 10Item features 1 7 2
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
RecSys - October 2014, Foster City, USA
Results Recommendation for New Users
22
MAE
0.00.20.40.60.81.01.21.41.61.82.02.22.4
STS CoMoDa Music
CAMF-CC SPF Content-based CAMF-CCDemographics-based CAMF-CC Average Weighted Heuristic SwitchingAdaptive Weighted
1-nD
CG
@1
0.00.10.20.30.40.50.60.70.80.91.0
STS CoMoDa Music
RecSys - October 2014, Foster City, USA
Results Recommendation for New Items
23
MAE
0.00.10.20.30.40.50.60.80.91.01.11.21.31.4
STS CoMoDa Music
CAMF-CC SPF Content-based CAMF-CCDemographics-based CAMF-CC Average Weighted Heuristic SwitchingAdaptive Weighted
1-nD
CG
@1
0.00.10.20.30.40.50.60.70.80.91.0
STS CoMoDa Music
RecSys - October 2014, Foster City, USA
Results Recommendation under New Contexts
24
MAE
0.00.10.20.30.40.50.70.80.91.01.11.2
STS CoMoDa Music
CAMF-CC SPF Content-based CAMF-CCDemographics-based CAMF-CC Average Weighted Heuristic SwitchingAdaptive Weighted
1-nD
CG
@1
0.00.10.20.30.40.50.60.70.80.91.0
STS CoMoDa Music
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
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
likes
FREERIDING
ALPING SKIING
likesMUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
FREERIDING
ALPING SKIING
likesMUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
FREERIDING
ALPING SKIING
likesMUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
ALPING SKIING
likesMUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
Skiing
ALPING SKIING
likes
Skiing
MUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
Skiing
ALPING SKIING
likes
similarSkiing
MUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
Skiing
ALPING SKIING
likes
similarlikely likesSkiing
MUSEUM
MUSEUM
likes
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
Skiing
ALPING SKIING
likes
similarlikely likesSkiing
MUSEUM
MUSEUM
likes Wet weather
Wet weather
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
Skiing
ALPING SKIING
likes
similarlikely likesSkiing
MUSEUM
MUSEUM
likes
similar
Wet weather
Wet weather
RecSys - October 2014, Foster City, USA
• 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)
Conclusions
26
SKIING
18-25 Male
18-25 Male
likes
similar
likely likes
FREERIDING
Skiing
ALPING SKIING
likes
similarlikely likesSkiing
MUSEUM
MUSEUM
likes
similarlikely likes
Wet weather
Wet weather
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
RecSys - October 2014, Foster City, USA
Questions?
Thank you.