ReComment: Towards Critiquing-based Recommendation with Speech Interaction

S C I E N C E

P A S S I O N

T E C H N O L O G Y

Critiquing-based
Recommendation
with Speech Interaction
Peter Grasch (peter.grasch@student.tugraz.at)
Alexander Felfernig (afelfern@ist.tugraz.at)
Florian Reinfrank (freinfra@ist.tugraz.at),
Institute for Software Technology
October 15, 2013

www.tugraz.at

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Speech Interaction in Recommender Systems
(Written) natural language input has shown promise
¨
˚
in (Shimazu 2001; arnestal, 2004)
Constraint satisfaction using spoken language
presented by Thompson et al in 2004

2

Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology
October 15, 2013

www.tugraz.at

Critiquing-based Recommender Systems
Pioneering work as early as 1984: M. Williams’
RABBIT [Williams, 1984]
Seminal work by Burke et al: FindMe
[Burke et al., 1997]
Continued, active research, especially in the areas of
advanced critiques
[McCarthy et al., 2004, Zhang and Pu, 2006] and
user modeling
[Reilly et al., 2005a, McCarthy et al., 2010]

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Traditional Critiquing-based Recommender

Figure : QwikShop [Reilly et al., 2005b]
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ReComment: Concept

Preference Elicitation

Preference Model

Recommendation
Strategy

Recommendation

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ReComment: Concept

Preference Elicitation

Preference Model

Recommendation
Strategy

Recommendation

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ReComment: Rationale
A speech-based natural language interface can allow
more expressive feedback, thus reducing session
length.

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ReComment: Recommendation Strategy
Incremental unit critiquing-based system
[Burke, 2000, Reilly et al., 2005a]
Prior probability based on sales rank
No initial search, relaxed similarity constraint
Custom utility function

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October 15, 2013

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ReComment: Recommendation Strategy
P ← {p ∈ P |p satisﬁes last given critique};
maxUtility ← −∞; bestOffer ← rold ;
for p ∈ P do
thisUtility ← ∞ ;
for c ∈ C do
c .age
thisUtility ← thisUtility + (1 − MaxAge ) ∗ c .utility (p) ;
end
if thisUtility > maxUtility then
maxUtility ← thisUtility ; bestOffer ← p ;
end
end
return bestOffer
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October 15, 2013

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ReComment: Utility Function
Control rate of change: Implicit goals
distance = distance(a.value, p[a.id ].value) ∗ r .direction;
perfectDist = metaModiﬁer ∗ 0.5;
if critiqueViolated then
return −abs(distance − perfectDist );
else
if distance < perfectDist then
return

distance
perfectDist ;

else
return max (perfectDist − distance + 1, 0.0001);
end
end
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October 15, 2013

Algorithm 1: Schematic utility calculation.

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Figure : Utility function of the critique x > 50.
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Figure : Utility function of subsequent critiques.
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Figure : Utility function of subsequent critiques.
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ReComment: Speech Processing
Speech recognition solution based on CMU SPHINX
and Simon [sph, 2013, sim, 2013]
Adapted to recommender situation
Keyword parser

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October 15, 2013

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Experiment
Comparison with traditional interface
80 participants
Measuring:
Interaction cycles
Perceived recommendation quality
Usability (adapted SUS)

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Experiment: Traditional User Interface

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Figure : ReComment: Mouse-based user interface.

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Experiment: Speech-based User Interface

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October 15, 2013

Figure : ReComment: Speech-based user interface.

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Experiment: Speech-based User Interface
Sentence
I am looking for a camera with 12 megapixel and
a weight of around 200 gram.
This camera with the same properties just
smaller.
An even smaller camera.
Optical zoom of 14 times would be better.
More optical zoom.
[...]
Table : Sample user interaction session.
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Results: Feedback Strategies

Category
Discarded
Unit critique
Compound critique (2 attributes)

Count
49 (12.8%)
329 (85.7%)
3 (0.8%)
2 (0.5%)
1 (0.3%)

Table : Types of used commands.

74 sentences (20 %) referred to explicit values.
12 sentences (3 %) used modiﬁers.
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"Recomment understands
my voice input"

Results: Speech Processing
25

24
20
15
10

10

5
0

3
0
1

2

3

4

Figure : Participants’ perception of the speech-recognition
accuracy ([1, 4], higher is better).
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October 15, 2013

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Results: Usability

Figure : Usability evaluation (adapted SUS scores).
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Results: Recommendation Quality
25

20
Score
1

15

2
3

10

4
5

0
Mouse−based interface

Speech−based interface

Figure : User score of last recommended item ([1, 4],
higher is better).
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Session length (cycles)

Results: Recommender Efﬁciency

50
40

47.7
34.5

30
20
10

9.025

7

Speech interface
(mean)

Speech interface
(median)

0
Mouse interface
(mean)

Mouse interface
(median)

Figure : Session length (lower is better).

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Conclusion

Spoken language recommender systems
are worth exploring!

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Future Work
Explore more natural user interfaces
Advanced sentiment analysis
Use of prosodic features, timing information, etc. to
infer certainty, frustration, etc.
Compare different recommender systems (e.g.,
constraint based approaches)

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Thank you for your attention.

Q&A

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Sources I
[sim, 2013] (2013).
About Simon — Simon.
http://simon.kde.org.
[sph, 2013] (2013).
CMU Sphinx - Speech Recognition Toolkit.
http://cmusphinx.sf.net.

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Sources II
[Burke, 2000] Burke, R. (2000).
Knowledge-based recommender systems.
In Encyclopedia of Library and Information
Systems. Marcel Dekker.
[Burke et al., 1997] Burke, R. D., Hammond, K. J.,
and Yound, B. (1997).
The ﬁndme approach to assisted browsing.
IEEE Expert, 12(4):32–40.
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Sources III
[McCarthy et al., 2004] McCarthy, K., Reilly, J.,
McGinty, L., and Smyth, B. (2004).
On the dynamic generation of compound critiques
in conversational recommender systems.
In Adaptive Hypermedia and Adaptive Web-Based
Systems, pages 176–184. Springer.

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Sources IV
[McCarthy et al., 2010] McCarthy, K., Salem, Y., and
Smyth, B. (2010).
Experience-based critiquing: reusing critiquing
experiences to improve conversational
recommendation.
In Case-Based Reasoning. Research and
Development, pages 480–494. Springer.

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Sources V
[Reilly et al., 2005a] Reilly, J., McCarthy, K., McGinty,
L., and Smyth, B. (2005a).
Incremental critiquing.
Knowledge-Based Systems, 18(4):143–151.

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Sources VI
[Reilly et al., 2005b] Reilly, J., Smyth, B., McGinty, L.,
and McCarthy, K. (2005b).
Critiquing with conﬁdence.
In Case-Based Reasoning Research and
Development, pages 436–450. Springer.

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Sources VII
[Williams, 1984] Williams, M. D. (1984).
What makes rabbit run?
International Journal of Man-Machine Studies,
21(4):333–352.

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Sources VIII
[Zhang and Pu, 2006] Zhang, J. and Pu, P. (2006).
A comparative study of compound critique
generation in conversational recommender
systems.
In Adaptive Hypermedia and Adaptive Web-Based
Systems, pages 234–243. Springer.

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ReComment: Towards Critiquing-based Recommendation with Speech Interaction

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