Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems

IntRS’15 - September 2015, Vienna, Austria
Parsimonious and Adaptive Contextual
Information Acquisition in Recommender
Systems
Matthias Braunhofer1
, Ignacio Fernández-Tobías2
and Francesco Ricci1

1
Free University of Bozen - Bolzano

Piazza Domenicani 3, 39100 Bolzano, Italy

{mbraunhofer,fricci}@unibz.it
2
Universidad Autónoma de Madrid

C / Francisco Tomás y Valiente 11, 28049 Madrid, Spain

ignacio.fernandezt@uam.es

Outline
2
• Introduction
• Related Works
• Selective Context Acquisition
• Experimental Evaluation and Results
• Conclusions and Future Work

Outline
2
• Introduction
• Related Works
• Conclusions and

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
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

Challenges for CARSs
4
• Identiﬁcation of contextual factors that inﬂuence user preferences and the
decision making process, and hence are worth to be collected from the users
along with their ratings
• Development of a predictive model for predicting the user’s ratings for items
under various contextual situations
• Design of a human-computer interaction layer on top of the predictive model

Example
STS (South Tyrol Suggests)
5
STS provides context-
aware suggestions for
Places Of Interest (POIs)
in South Tyrol, Italy

Example
STS w/o Selective Context Acquisition
6
Don’t.
All contextual factors are
requested.

Example
STS w/ Selective Context Acquisition
7
Do.
Only relevant contextual
factors are requested.

Outline
8
• Related Works
• Introduction

Context Selection
A Priori (i.e., Before Collecting Ratings)
• (Baltrunas et al., 2012): Development of a
web survey where users were requested to
evaluate the influence of contextual
conditions on POI categories

• This allowed to identify the relevant
contextual factors for different POI
categories (using mutual information
statistic)

• Pros: can acquire ratings under relevant
contextual conditions
• Cons: artificial setting; survey requires extra
effort from the user
9

Context Selection
A Posteriori (i.e., After Collecting Ratings)
• (Odić et al., 2013): Provision of several
statistic-based methods for detection of
relevant context, i.e., unalikeability,
entropy, sample variance, χ2
test,
Freeman–Halton test
• Results show a signiﬁcant diﬀerence in
prediction of ratings in context detected as
relevant and the one detected as irrelevant

• Pros: can improve rating prediction
• Cons: still irrelevant context is acquired in
the rating acquisition phase
10
Relevant context Unclassified context
Irrelevant context Baseline predictors

Outline
11
• Related Works
• Conclusions and F
• Introduction

Parsimonious & Adaptive Context Acquisition
• Main idea: for each user-item pair (u, i), identify the contextual factors that
when acquired together with u’s rating for i improve most the overall system
• Heuristic: acquire the contextual factors that have the largest impact on
rating prediction

• Example:
12
(Alice, Skiing)
Season
Weather
Temperature
Daytime
Impact
0.000 0.125 0.250 0.375 0.500

Parsimonious & Adaptive Context Acquisition
• Main idea: for each user-item pair (u, i), identify the contextual factors that
when acquired together with u’s rating for i improve most the overall system
• Heuristic: acquire the contextual factors that have the largest impact on
rating prediction

• Example:
12
(Alice, Skiing)
Season
Weather
Temperature
Daytime
Impact
0.000 0.125 0.250 0.375 0.500
How to
quantify this
impact?

CARS Prediction Model
• We use a new variant of Context-Aware Matrix Factorization (CAMF)
(Baltrunas et al., 2011) that treats contextual conditions similarly to either item
or user attributes

• Advantage: allows to capture latent correlations and patterns between a
potentially wide range of knowledge sources ⟹ ideal to derive the usefulness
of contextual factors
13
ˆruic1,...,ck
= (qi + xa
a∈A(i)∪C(i)
∑ )T
⋅(pu + yb
b∈A(u)∪C(u)
∑ )+ ri + bu
qi latent factor vector of item i

A(i) set of conventional item attributes (e.g., genre)

C(i) set of contextual item attributes (e.g., weather)

xa latent factor vector of item attribute a

pu latent factor vector of user u

A(u) set of conventional user attributes (e.g., age)

C(u) set of contextual user attributes (e.g., mood)

yb latent factor vector of user attribute b

ṝi average rating for item i

bu baseline for user u

Largest Deviation
• Computes a personalized relevance score for a contextual factor Cj and a
user-item pair (u, i)

• Given (u, i), it ﬁrst measures the “impact” of each contextual condition cj ∈ Cj
by calculating the absolute deviation between the rating prediction when the
condition holds (i.e., ȓuicj) and the predicted context-free rating (i.e., ȓui):

where fcj is the normalized frequency of cj

• Finally, it takes the average of these individual scores for the contextual
conditions to yield a single relevance score for the contextual factor Cj
14
ˆwuicj
= fcj
ˆruicj
− ˆrui ,

Illustrative Example
• ȓAlice Skiing Sunny = 5
• ȓAlice Skiing = 3.5
• 20% of ratings are tagged with Sunny (i.e., fSunny = 0.2)

• ŵAlice Skiing Sunny = 0.2⋅|5 - 3.5| = 0.3
15

Outline
16
• Introduction
• Related Works
• Conclusions

CoMoDa TripAdvisor
Domain Movies POIs
Rating scale 1-5 1-5
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
Contextual factors 12 3
Contextual conditions 49 31
User attributes 4 2
Item features 7 12
Datasets
17

CoMoDa TripAdvisor
Domain Movies POIs
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
User attributes 4 2
Item features 7 12
Datasets
17
time,
daytype, season,
location, weather,
social, mood, …

CoMoDa TripAdvisor
Domain Movies POIs
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
User attributes 4 2
Item features 7 12
Datasets
17
age, gender, city,
country

CoMoDa TripAdvisor
Domain Movies POIs
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
User attributes 4 2
Item features 7 12
Datasets
17
director, country,
language, year, budget,
genres, actors

CoMoDa TripAdvisor
Domain Movies POIs
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
User attributes 4 2
Item features 7 12
Datasets
17
type, month and year
of the trip

CoMoDa TripAdvisor
Domain Movies POIs
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
User attributes 4 2
Item features 7 12
Datasets
17
user location, member
type

CoMoDa TripAdvisor
Domain Movies POIs
Ratings 2,098 4,147
Users 112 3,916
Items 1,189 569
User attributes 4 2
Item features 7 12
Datasets
17
item type,
amenities, item
locality, price range,
hotel class, …

Evaluation Procedure
Overview
18

Overview
18
• Repeated random sub-sampling validation (20 times):

Overview
18
25% 50% 25%
Training set Candidate set Testing set
• Randomly partition the ratings into three subsets

Overview
18
25% 50% 25%
• For each user-item pair (u,i) in the candidate set, compute the N most relevant
contextual factors and transfer the corresponding rating and context information ruic
in the candidate set to the training set as ruic' with c' ⊆ c containing the associated
contextual conditions for these factors

Overview
18
25% 50% 25%
• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the testing
set, after training the prediction model on the new extended training set

Overview
18
25% 50% 25%
• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the testing
set, after training the prediction model on the new extended training set
• Repeat

user-item pair
top two contextual factors
rating transferred to training set
Example
19
+
+
=
rating in candidate set

(Alice, Skiing)
top two contextual factors
Example
19
+
+
=

(Alice, Skiing)
Season and Weather
Example
19
+
+
=

(Alice, Skiing)
Season and Weather
Example
19
rAlice Skiing Winter, Sunny, Warm, Morning = 5+
+
=

(Alice, Skiing)
Season and Weather
Example
19
rAlice Skiing Winter, Sunny, Warm, Morning = 5
rAlice Skiing Winter, Sunny = 5
+
+
=

Baseline Methods for Evaluation
• Mutual Information (Baltrunas et al., 2012): given a user-item pair (u,i), it
computes the relevance score for the contextual factor Cj as the normalized
mutual information between the ratings for items belonging to i’s category
and Cj

• Freeman-Halton Test (Odić et al., 2013): calculates the relevance of a
contextual factor Cj using the Freeman-Halton test, which is the Fisher’s exact
test extended for contingency tables > 2 × 2

• Minimum Redundancy Maximum Relevance - mRMR (Peng et al., 2005):
ranks each contextual factor Cj according to its relevance to the rating
variable and redundancy to other contextual factors

• Random: randomly chooses the top N contextual factors for a user-item pair
20

Evaluation Results
U-MAE
21
CoMoDa
U-MAE
0.71
0.72
0.73
0.74
0.75
0.76
0.77
0.78
0.79
0.80
0.81
0.82
Number of Selected Contextual Factors
1 2 3 4
Largest Deviation Mutual Information Freeman-Halton mRMR Random All features
TripAdvisor
U-MAE
0.520
0.521
0.522
0.523
0.524
0.525
0.526
0.527
0.528
0.529
0.530
0.531
0.532
0.533
1 2 3
*
*
* * *
*
*
*
*
*
*
*

Evaluation Results
Precision@10
22
CoMoDa
Precision@10
0.0000
0.0002
0.0004
0.0006
0.0008
0.0010
0.0012
0.0014
0.0016
1 2 3 4
TripAdvisor
Precision@10
0.0100
0.0105
0.0110
0.0115
0.0120
0.0125
0.0130
0.0135
0.0140
0.0145
0.0150
0.0155
0.0160
1 2 3
*
*
*
*
*
* *
*
*
*

Evaluation Results
Recall@10
23
CoMoDa
Recall@10
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
1 2 3 4
TripAdvisor
Recall@10
0.100
0.105
0.110
0.115
0.120
0.125
0.130
0.135
0.140
0.145
0.150
0.155
0.160
1 2 3
*
*
*
*
* *
*
*
*
*

Evaluation Results
Practical Implications
• Using Largest Deviation, we know that we can ask only the contextual factors
C1, C2 and C3 when we ask user u to rate item i
24

Outline
25
• Introduction
• Related Works

Conclusions
• Identifying which contextual factors should be acquired from the user upon
rating an item is an important and practical problem for CARSs

• We tackled this problem with a new method that asks the user to specify
those contextual factors that if considered in the CARS prediction model
would produce a rating prediction that is most different from the context-free
prediction

• Results from our offline experiment confirm that the proposed parsimonious
context acquisition strategy elicits ratings with contextual information that
improve more the recommendation performance
26

Future Work
• Evaluate the performance of employing an Active Learning method for
adaptively selecting both the item to rate and the contextual information to
add

• Understand how the proposed method can be extended to generate requests
for contextual data that takes into account possible correlations between
contextual factors

• Update the evaluation procedure so that it can be used also on rating
datasets for which only a subset of contextual factors is known

• Integrate the developed method into our STS app and perform a live user
study
27

Questions?
Thank you.

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