hybrid solution of the cold-start problem in context-aware recommender systems

UMAP Doctoral Consortium - July 2014, Aalborg, Denmark

Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems

Matthias Braunhofer!

Free University of Bozen - BolzanoPiazza Domenicani 3, 39100 Bolzano, Italy

[email protected]

mailto:[email protected]


Outline

2

• Context-Aware Recommenders and the Cold-Start Problem

• Related Work

• Basic Context-Aware Rating Prediction Models

• Hybrid Context-Aware Rating Prediction Models

• Conclusions and Open Issues


• 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

• Three basic approaches:

• Contextual pre-filtering

• Contextual post-filtering

• Contextual modelling

Context-Aware Recommender Systems

3


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?

4

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


Our Solution: Hybrid CARS

• Ultimate goal: design and development of hybrid CARSs that combine different CARS algorithms depending on their estimated strengths and weaknesses to predict a user’s rating for an item given a particular cold-start situation

• Example:

5

(user, item, context) tuple

CARS 1

CARS 2

Combination Final score

Score

Score

Hybrid CARS


Key Steps

• Identify candidate basic context-aware rating prediction models

• Analyse candidate rating prediction models (what are their strengths and weaknesses under cold-start situations?)

• Design, develop and evaluate novel hybrid CARSs

• Integrate the best-performing hybrid CARS into our STS (South Tyrol Suggests) mobile app

• Evaluate it through a live user study

6


Outline

7

• Context-Aware Recommenders

• Related Work


• Hybrid



Related Work

8

Cold-starting CARSs

… using additional data

… better processing known data

Active learning(Elahi et al., 2013)

Cross-domain rec.(Enrich et al., 2013)

User / item attributes(Woerndl et al., 2009)

Context similarities(Zheng et al., 2013)(Codina et al., 2013)


Outline

9


• Related Work





CAMF-CC (Baltrunas et al., 2011)

• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a variant of CAMF that extends standard Matrix Factorization (MF) by incorporating baseline parameters for contextual condition-item category pairs

10

r̂uic1,...,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


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

11

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


Category-based CAMF-CC

• It is a novel variant of CAMF-CC that incorporates additional sources of information about the items, i.e., category or genre information

• Conjecture: alleviates the new item problem of CAMF-CC

12

r̂uic1,...,ck = (qi + xt )t∈T (i )∑ T

pu + µ + bi + bu + btcjj=1

k

∑t∈T (i )∑

qi latent factor vector of item iT(i) set of categories associated to item ixt latent factor vector of item category tpu latent factor vector of user uμ overall average ratingbi baseline for item ibu baseline for user ubtcj baseline for item category-contextual condition tcj


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

13

r̂uic1,...,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


Evaluation Discussion

• Offline evaluation of cold-start performance of CARSs is a complex task:

• Not done before

• Requires large (enough) contextually-tagged rating datasets with user and item attributes

• Must consider multiple perspectives: new users, new items, new contextual situations, mixtures of elementary cold-start cases, different degrees of coldness, different types of user and item attribute information available

14


• 2 contextually-tagged rating datasets

STS (Elahi et al., 2013)

LDOS-CoMoDa (Odić et al., 2013)

Domain POIs MoviesRating scale 1-5 1-5Ratings 2,422 2,296Users 305 121Items 238 1,232Contextual factors 14 12Contextual conditions 57 49Contextual situations 880 1,969User attributes 7 4Item features 1 7

Evaluation Used Datasets

15


Evaluation Evaluation Procedure

• Five-fold cross-validation where proper subsets of the testing set are used, depending on the cold-start situation under consideration

• Divide the ratings into five cross-validation folds

• For each fold k = 1, 2, …, 5

• Use all ratings except those in fold k to train the prediction models

• Calculate the Mean Absolute Error (MAE) on those ratings in fold k that are coming from new users, new items and new contextual situations, respectively

• Users, items or contextual situations are new if they have at most n ratings in the training set, with n ranging from 0 to 10

• Advantage: allows to test for different degrees of coldness

• Drawback: small testing sets are filtered and get even smaller

16


Evaluation Obtained Results (1/3)

MAEs for new users

17

CoMoDa

MAE

0.65

0.75

0.85

0.95

1.05

1.15

1.25

User profile size0 1 2 3 4 5 6 7 8 9 10

MF CAMF-CC SPF Category-based CAMF-CC Demographics-based CAMF-CC

STS

MAE

0.65

0.75

0.85

0.95

1.05

1.15

1.25

User profile size0 1 2 3 4 5 6 7 8 9 10



MAEs for new items

18

CoMoDa

MAE

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

Item profile size0 1 2 3 4 5 6 7 8 9 10


STS

MAE

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

Item profile size0 1 2 3 4 5 6 7 8 9 10



MAEs for new contextual situations

19

CoMoDa

MAE

0.70

0.75

0.80

0.85

0.90

0.95

Context profile size0 1 2 3 4 5 6 7 8 9 10


STS

MAE

0.70

0.75

0.80

0.85

0.90

0.95

Context profile size0 1 2 3 4 5 6 7 8 9 10


Outline

20


• Related Work





Heuristic Switching*

• Main idea: use a stable heuristic to switch between the basic CARS algorithms depending on the encountered cold-start situation

21


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

* Described in our short paper submitted to ACM RecSys 2014


Adaptive Weighted*

• Main idea: adaptively weight each basic CARS algorithm based on how well it performs for the user, item and contextual situation in question

22


CAMF-CC

SPF

Content-CAMF-CC

Demogr.-CAMF-CC

Adapter

Adapter

Adapter

Adapter

Score

Score

Score

Score

(Score, Weight)

(Score, Weight)

(Score, Weight)

(Score, Weight)∑ Final score

Algorithms layer Adaptive layer Aggregation

* Described in our paper submitted to ACM RecSys 2014 Doctoral Symposium


• 3 contextually-tagged rating datasets

STS (Elahi 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 139Items 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

Evaluation Used Datasets

23


Evaluation Evaluation Procedure

24

• Randomly split users / items / contexts into training set and testing set → creates a set of users / items / contexts in the testing set that have no ratings in the training set

• Advantage: the entire rating dataset can be used

• Drawback: can’t test for different degrees of coldness

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

New user test New item test New context test

Training set Testing set


Evaluation Summary of Obtained Results

• Significant differences in normalised Discounted Cumulative Gain (nDCG) and MAE between basic CARS algorithms across different cold-start cases

• Content-based CAMF-CC works best for the new item situation

• Demographics-CAMF-CC works best both for the new user and new context situation

• Hybridisation techniques can improve performance

• In almost all cases, they outperformed the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF), thus easing the problem of model selection

25


Outline

26


• Related Work





Conclusions

• Basic CARS algorithms perform very differently in the different cold-start situations

• Knowledge of strengths and weaknesses of each basic CARS algorithm in the various cold-start situations allows the development of hybrid techniques

• First developed and tested hybrid CARS algorithms are able to outperform the state-of-the-art CARS algorithms (i.e., CAMF-CC and SPF)

27


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

28


Questions or Comments?

Thank you.

hybrid solution of the cold-start problem in context-aware recommender systems

Internet

umap doctoral consortium

new context problem

denmark cold

item categories

new item problem of

context tuple cars

item ibu baseline

denmark outline