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ReComment: Towards Critiquing-based Recommendation with Speech Interaction
 

ReComment: Towards Critiquing-based Recommendation with Speech Interaction

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    ReComment: Towards Critiquing-based Recommendation with Speech Interaction ReComment: Towards Critiquing-based Recommendation with Speech Interaction Presentation Transcript

    • 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
    • www.tugraz.at 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] 3 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Traditional Critiquing-based Recommender Figure : QwikShop [Reilly et al., 2005b] 4 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at ReComment: Concept Preference Elicitation Preference Model Recommendation Strategy Recommendation 5 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at ReComment: Concept Preference Elicitation Preference Model Recommendation Strategy Recommendation 6 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at ReComment: Rationale A speech-based natural language interface can allow more expressive feedback, thus reducing session length. 7 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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 8 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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 9 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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 10 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013 Algorithm 1: Schematic utility calculation.
    • www.tugraz.at ReComment: Utility Function Control rate of change: Implicit goals Figure : Utility function of the critique x > 50. 11 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at ReComment: Utility Function Control rate of change: Implicit goals Figure : Utility function of subsequent critiques. 12 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at ReComment: Utility Function Control rate of change: Implicit goals Figure : Utility function of subsequent critiques. 13 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at ReComment: Speech Processing Speech recognition solution based on CMU SPHINX and Simon [sph, 2013, sim, 2013] Adapted to recommender situation Keyword parser 14 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Experiment Comparison with traditional interface 80 participants Measuring: Interaction cycles Perceived recommendation quality Usability (adapted SUS) 15 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Experiment: Traditional User Interface 16 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013 Figure : ReComment: Mouse-based user interface.
    • www.tugraz.at Experiment: Speech-based User Interface 17 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013 Figure : ReComment: Speech-based user interface.
    • www.tugraz.at 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. 18 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Results: Feedback Strategies Category Discarded Unit critique Compound critique (2 attributes) Compound critique (3 attributes) Compound critique (5 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. 19 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at "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). 20 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Results: Usability Figure : Usability evaluation (adapted SUS scores). 21 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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). 22 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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). 23 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Conclusion Spoken language recommender systems are worth exploring! 24 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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) 25 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Thank you for your attention. Q&A 26 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Sources I [sim, 2013] (2013). About Simon — Simon. http://simon.kde.org. [sph, 2013] (2013). CMU Sphinx - Speech Recognition Toolkit. http://cmusphinx.sf.net. 27 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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. 28 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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. 29 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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. 30 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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. 31 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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. 32 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at Sources VII [Williams, 1984] Williams, M. D. (1984). What makes rabbit run? International Journal of Man-Machine Studies, 21(4):333–352. 33 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013
    • www.tugraz.at 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. 34 Peter Grasch (peter.grasch@student.tugraz.at), Institute for Software Technology October 15, 2013