Conversational Recommender Systems 2016


Notes:

  • Conversational recommender
  • Conversational recommenders
  • Conversational recommender system (CRS)

Wikipedia:

References:

See also:

100 Best Conversational Commerce Videos | 100 Best Recommender System Videos | Conversational Agent Timeline | Conversational Agents 2016 | Patents Search for Conversational Agents 2016 | Conversational Interface & Dialog Systems 2016 | Conversational Systems Meta Guide | Conversational Intelligence | Recommender Dialog Systems 2016


Towards Conversational Recommender Systems.
K Christakopoulou, F Radlinski, K Hofmann – KDD, 2016 – chara.cs.illinois.edu
ABSTRACT People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such

Conversational Recommender System with Explanation Facility Using Semantic Reasoning
N Rahmawati – International Journal on Information …, 2016 – socj.telkomuniversity.ac.id
Abstract Conversational recommender system is system that provides dialogue as user guide to obtain information from the user, in order to obtain preference for products needed. This research implements conversational recommender system with knowledge-based in

Design of knowledge for conversational recommender system based on product functional requirements
ZKA Baizal, DH Widyantoro… – Data and Software …, 2016 – ieeexplore.ieee.org
Abstract: Conversational recommender system (CRS) has been developed to simulate conversation between a customer with a professional sales support. For most customers, specifying needs based on product functional requirements is more natural way rather than

Compound Critiquing for Conversational Recommender System Based on Functional Requirement
YR Murti, ZK Baizal – Advanced Science Letters, 2016 – ingentaconnect.com
Today, it is not difficult to find new products with different functions and features. This condition puts consumers in doubt when they have to decide what product of which the specification and features meet their expectation. Conversational recommender system

Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements
ZKA Baizal, DH Widyantoro… – … Electronics and Control), 2016 – journal.uad.ac.id
Abstract Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (eg, cars, smartphones, notebook, etc.), most users are

An Experience-Based Critiquing Approach to Conversational Recommendation
Y Salem, J Hong, W Liu – 2016 – researchgate.net
… This type of conversational recommender systems are much better suited for helping users navigate complex product spaces … Chapter 2. Background and Related Work 2.1 Conversational Recommender Systems Various forms of feedback are used in conversational recom …

Query refinement in recommender system based on product functional requirements
ZKA Baizal, DH Widyantoro… – … Computer Science and …, 2016 – ieeexplore.ieee.org
… Abstract One of the advantages of conversational recommender system (CRS) is its ability to guide user in expressing their needs through conversation mechanism. In many cases, customers are often unable to express their needs clearly at the beginning of the interaction …

Conversational recommendation to avoid the cold-start problem
F Benito-Picazo, M Enciso, C Rossi, A Guevara – researchgate.net
… This situation also occurs in systems where users make occasional use. An interesting approach to solve this problem us the use of the so-called conversational recommender systems [7, 8]. These are closely related with critiquing recommender sys- tems [17, 21] …

Improving Navigation in Critique Graphs
B Genc, B O’Sullivan – Tools with Artificial Intelligence (ICTAI) …, 2016 – ieeexplore.ieee.org
… and how they can be used to analyse conversational recommenders, as well as offer advice on an ideal set of entry products to present to the user at the outset. However, critique graphs also offer the promise of assisting the designers of conversational recommender systems to …

Learning a Region of User’s Preference for Product Recommendation.
A Sekar, S Chakraborti – ICCBR Workshops, 2016 – ceur-ws.org
… Abstract. A Conversational Recommender System(CRS) aims to sim- ulate the real world setting where a shopkeeper tries to learn the prefer- ences of a user through conversation and the user, in turn, learns about the product base …

Towards Fast Algorithms for the Preference Consistency Problem Based on Hierarchical Models.
AM George, N Wilson, B O’Sullivan – IJCAI, 2016 – ijcai.org
… The Preference De- duction Problem (PDP) aims at eliciting only few prefer- ences and inferring more preferences from the given ones; this might then be used in a conversational recommender sys- tem, for example, to help choose which items to show to the user next [Bridge …

M-Commerce: Multimedia technology using cloud based recommendation system
DR Jariwala, BP Patel – rexjournal.org
… contributions in Robotics, Electronics and Computer Science, such as the invention of smarter environments, appliances and devices [5]. A typical session with a conversational recommender consists of a series of recommend-review-revise-update cycles …

An Architecture for a Mobile Recommender System in Tourism
M Hosseini, SMR Mousavi, F Zolrahmi – International Journal of …, 2016 – journal.itrc.ac.ir
… In E-Commerce and Web Technologies Springer Berlin Heidelberg; 2012, pp. 88-99. [24] Jannach, D & , Kreutler, G. “Rapid development of knowledge-based conversational recommender applications with advisor suite” . Journal …

Facilitating safe adaptation of interactive agents using interactive reinforcement learning
K Tsiakas – Companion Publication of the 21st International …, 2016 – dl.acm.org
… reward. In Proc. AAMAS (2012), 475–482. [8] Mahmood, T., Mujtaba, G., and Venturini, A. Dynamic personalization in conversational recommender systems. Inf.Sys and e-Business Management 12, 2 (2014), 213–238. [9] Martin …

Structured feedback for preference elicitation in complex domains
S Teso, P Dragone, A Passerini – BeyondLabeler Workshop at IJCAI, 2016 – disi.unitn.it
… Given its reliance on ranking constraints, coac- tive learning naturally falls within our structured constraint framework. Another group of related approaches is critiquing-based (or conversational) recommender systems [Viappiani et al., 2006; Chen and Pu, 2012] …

Inferring Users’ Critiquing Feedback on Recommendations from Eye Movements
L Chen, F Wang, W Wu – International Conference on Case-Based …, 2016 – Springer
… to obtaining users’ feedback on recommendations is critiquing, which has become the core feedback mechanism in so called conversational recommenders [17, 22] and … Mahmood, T., Mujtaba, G., Venturini, A.: Dynamic personalization in conversational recommender systems …

Learning Trustworthy Behaviors Using an Inverse Trust Metric
MW Floyd, M Drinkwater, DW Aha – Robust Intelligence and Trust in …, 2016 – Springer
… Conversational recommender systems (McGinty and Smyth 2003) use interactions with a user to tailor recommendations to the user’s preferences … Similar to conversational recommender systems, learning interface agents are designed to be assistive with one specific task …

I’ll know it when I see it: Toward cognitively plausible recommendations
SL Epstein, E Osisek – cognitum.ws
… et al., 2003]. Al- ternatively, a conversational recommender conducts an ini- tial dialogue with the user to determine her current prefer- ences. Such a dialogue can assist with both cold starts and gray sheep. To date, however …

Incorporating user experience into critiquing-based recommender systems: a collaborative approach based on compound critiquing
H Xie, DD Wang, Y Rao, TL Wong, LYK Raymond… – International Journal of … – Springer
… terms of reducing the interaction effort of users. Keywords. Collaborative approach Compound critiquing Conversational recommender Retrospective user study. 1 Introduction. Product recommender systems (RSs) [1, 36] have …

ADVANCED SCIENCE LETTERS
C Roeksukrungrueang, S Chivapreecha – umexpert.um.edu.my
… 22, 1887–1891 (2016) 1892–1896 Compound Critiquing for Conversational Recommender System Based on Functional Requirement … Conversational recommender system (CRS) helps users find the products that meet their criteria by using ques- tion and answer interaction …

MOOCs Platforms Study Toward Building MOOCs Recommender Portal
HC Ouertani, MM Alawadh – International Conference on …, 2016 – search.proquest.com
… REFERENCES. [1] McGinty, L. and Smyth, B., 2003: “On the Role of Diversity in Conversational Recommender Systems”. [2] Alexandra, C., June 2000: “Recommender Systems, CSE435: Intelligent Decision Support Systems” …

Automatic text classification of ICD-10 related CoD from complex and free text forensic autopsy reports
G Mujtaba, L Shuib, RG Raj… – … (ICMLA), 2016 15th …, 2016 – ieeexplore.ieee.org
… on, 2011, pp. 1-6. [5] T. Mahmood, G. Mujtaba, and A. Venturini, “Dynamic personalization in conversational recommender systems,” Information Systems and e-Business Management, vol. 12, pp. 213-238, 2014. [6] G. Mujtaba …

Approaches, Issues and Challenges in Recommender Systems: A Systematic Review
B Kumar, N Sharma – Indian Journal of Science and Technology, 2016 – indjst.org
Page 1. *Author for correspondence Indian Journal of Science and Technology, Vol 9(47), DOI: 10.17485/ijst/2016/v9i47/94892, December 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Approaches, Issues and Challenges in Recommender …

Recommender systems—: beyond matrix completion
D Jannach, P Resnick, A Tuzhilin… – Communications of the …, 2016 – dl.acm.org
… the recommendation service. In the research literature, conversational recommender systems were proposed to elicit user preferences interactively and engage in a “propose, feedback, and revise” cycle with users. 37 They …

Preference Inference Based on Pareto Models
AM George, N Wilson – International Conference on Scalable Uncertainty …, 2016 – Springer
… In the Preference Deduction Problem (PDP), the idea is to elicit only a few preferences from the user and infer other preferences; this might then be used in a conversational recommender system, for example, to help choose which items to show to the user next …

Adaptable and Adaptive Human-Computer Interface to Recommend Learning Objects from Repositories
T Quiroz, OM Salazar, DA Ovalle – International Conference on Learning …, 2016 – Springer
… parameters. In: Proceedings of the Workshop Embedding User Models in Intelligent Applications, pp. 24–29 (1997). 3. Smyth, B., McGinty, L., Reilly, J., McCarthy, K.: Compound critiques for conversational recommender systems. In …

Ontology-based recommendation involving consumer product reviews
ZKA Baizal, A Iskandar… – … Technology (ICoICT), 2016 …, 2016 – ieeexplore.ieee.org
… 39, no.4, 2012, pp.3995-4006 [8] DH Widyantoro, ZKA Baizal, “A framework of conversational recommender system based on user functional requirements,”In: 2nd IEEE International Conference on Information and Communication Technology (ICoICT), IEEE, 2014, pp …

Control of proclivity toward selling electricity using persuasive dialog system
K Kitagawa, K Kogiso – Control Systems (ISCS), 2016 SICE …, 2016 – ieeexplore.ieee.org
… and Understanding, pp. 114–119, 2013. [7] P. Warnest, “ User evaluation of a conversational recommender system”, Proceedings of Workshop on Knowledge and Reasoing in Pratical Dialogue Sytems, pp. 32–39, 2005. [8] S …

Training Infrastructure to Participate in Real Life Institutions: Learning through Virtual Worlds
P Almajano, M Lopez-Sanchez… – … of Research on 3-D …, 2016 – books.google.com
Page 220. 192 Chapter 8 Training Infrastructure to Participate in Real Life Institutions: Learning through Virtual Worlds Pablo Almajano University of Barcelona, Spain Maite Lopez-Sanchez University of Barcelona, Spain Inmaculada …

The New Challenges when Modeling Context through Diversity over Time in Recommender Systems
A L’Huillier, S Castagnos, A Boyer – … of the 2016 Conference on User …, 2016 – dl.acm.org
… Revue des Sciences et Technologies de l’Information, 2016. [8] L. McGinty and B. Smyth. On the role of diversity in conversational recommender systems. In Proceedings of the Fifth International Conference on Case–Based Reasoning, pages 276–290. Springer, 2003 …

Preferences in Case-Based Reasoning
A Abdel-Aziz – 2016 – d-nb.info
Page 1. Preferences in Case-Based Reasoning Amira Abdel-Aziz Fachbereich Mathematik und Informatik Philipps-Universität Marburg Dissertation zur Erlangung eines Doktorgrades der Naturwissenschaften (Dr. rer. nat.) Marburg/Lahn, 2016 Page 2 …

Dynamic Control of Proclivity toward Selling Electricity Using Persuasive Dialogue System
K Kitagawa, K Kogiso – SICE Journal of Control, Measurement, and …, 2016 – jstage.jst.go.jp
… 114–119, 2013. Page 7. SICE JCMSI, Vol. 9, No. 6, November 2016 270 [13] P. Warnest: User evaluation of a conversational recommender system, Proceedings of Workshop on Knowledge and Reasoing in Pratical Dialogue Sytems, pp. 32–39, 2005 …

IaaS Cloud Service Selection using Case-Based Reasoning
S Soltani – 2016 – qspace.library.queensu.ca
Page 1. IAAS CLOUD SERVICE SELECTION USING CASE-BASED REASONING By Sima Soltani A thesis submitted to the School of Computing in conformity with the requirements for the degree of Doctor of Philosophy Queen’s University Kingston, Ontario, Canada …

Interactive Recommending: Framework, State of Research and Future Challenges.
B Loepp, CM Barbu, J Ziegler – EnCHIReS@ EICS, 2016 – researchgate.net
… 43. Sen, S., Vig, J., & Riedl, J. Tagommenders: Connect- ing users to items through tags. In WWW ’09, ACM (2009), 671–680. 44. Smyth, B., & McGinty, L. An analysis of feedback strategies in conversational recommenders. In AICS ’03 (2003). 45 …

User Control in Recommender Systems: Overview and Interaction Challenges
D Jannach, S Naveed, M Jugovac – International Conference on Electronic …, 2016 – Springer
… In: IEEE ICDEW Data Engineering Workshop, pp. 801–810 (2007). 21. Jannach, D., Kreutler, G.: Rapid development of knowledge-based conversational recommender applications with Advisor Suite. J. Web Eng. 6(2), 165–192 (2007)Google Scholar. 22 …

Data Mining: A Book Recommender System Using Frequent Pattern Algorithm
JV Joshua, OD Alao, AO Adebayo, GA Onanuga… – pdfs.semanticscholar.org
… Mahmood, T., Ricci, F. (2007)“Towards learning user-adaptive state models in a conversational recommender system.” In: A Hinneburg (ed.) LWA 2007: Lernen – Wissen – Adaption, Halle, September 2007, Workshop Proceedings, 2007, pp …

Incorporating transparency during trust-guided behavior adaptation
MW Floyd, DW Aha – International Conference on Case-Based Reasoning, 2016 – Springer
… While many recommender systems explain why they gave a particular recommendation, explanations have also been used to explain why follow-up questions were asked in conversational recommender systems [19]. Muhammad et al …

Explanations in recommender systems: an overview
R Sharma, S Ray – International Journal of Business …, 2016 – inderscienceonline.com
… Page 12. Explanations in recommender systems 259 • Another exciting area will be to understand how observations from interactions with conversational recommender systems (by allowing users to choose their tags and preferences) can be exploited to improve explanations …

Case retrieval algorithm using similarity measure and adaptive fractional brain storm optimization for health informaticians
P Yadav – Arabian Journal for Science and Engineering, 2016 – Springer
Page 1. Arab J Sci Eng (2016) 41:829–840 DOI 10.1007/s13369-015-1928-y RESEARCH ARTICLE – COMPUTER ENGINEERING AND COMPUTER SCIENCE Case Retrieval Algorithm Using Similarity Measure and Adaptive …

Collaborative and Social Mobility Services
D Herzog, W Wörndl – Digital Mobility Platforms and …, 2016 – mediatum.ub.tum.de
… [20] Francesco Ricci and Quang Nhat Nguyen. Mobyrek: A conversational recommender system for on-the-move travellers. In DR Fesenmaier, H. Werthner,, and KW Wober, editors, Recommendation Systems: Behavioural Foundations and Applications. CABI Publishing, 2006 …

Emotion Elicitation in Socially Intelligent Services: the Intelligent Typing Tutor Study Case.
A Kosir, M Meza, J Kosir, M Svetina… – EMPIRE …, 2016 – pdfs.semanticscholar.org
Page 1. Emotion Elicitation in Socially Intelligent Services: the Intelligent Typing Tutor Study Case Andrej Košir University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25 Ljubljana, Slovenia andrej.kosir@fe.uni-lj.si …

A Survey on Recommender System
M Sridevi, RR Rao, MV Rao – International Journal of …, 2016 – search.proquest.com
… [64] K McCarthy, J Reilly, L McGinty, and B Smyth. Thinking positively – explanatory feedback for conversational recommender systems. In Proceedings of the European Conferenceon Case-Based Reasoning (ECCBR-04) ExplanationWorkshop, 2004 …

Preferences in artificial intelligence
G Pigozzi, A Tsoukias, P Viappiani – Annals of Mathematics and Artificial …, 2016 – Springer

Knowledge-based recommender systems
CC Aggarwal – Recommender Systems, 2016 – Springer

A distributed and multi?tiered software architecture for assessing e?Commerce recommendations
L Palopoli, D Rosaci, GML Sarné – … and Computation: Practice …, 2016 – Wiley Online Library
By continuing to browse this site you agree to us using cookies as described in About Cookies. Remove maintenance message …

Preference inference through rescaling preference learning
N Wilson, M Montazery – Proceedings of the Twenty-Fifth International …, 2016 – cora.ucc.ie
Page 1. Title Preference inference through rescaling preference learning Author(s) Wilson, Nic; Montazery, Mojtaba Publication date 2016 Original citation Wilson, N. and Montazery, M. (2016) ‘Preference inference through rescaling …

An Automated Recommender System for Course Selection
A Al-Badarenah, J Alsakran – International Journal of Advanced …, 2016 – researchgate.net
Page 1. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 3, 2016 166 | P age www.ijacsa.thesai.org An Automated Recommender System for Course Selection Amer Al-Badarenah …

The impact of profit incentives on the relevance of online recommendations
U Panniello, S Hill, M Gorgoglione – Electronic Commerce Research and …, 2016 – Elsevier
Recommender systems are commonly used by firms to improve consumers’ online shopping experiences, with the secondary benefit of increased sales and profits. P.

Understanding the role of latent feature diversification on choice difficulty and satisfaction
MC Willemsen, MP Graus, BP Knijnenburg – User Modeling and User …, 2016 – Springer
… Bridge and Kelly (2006) similarly investigated the role of diversification and showed that different methods of diversifying collaborative filtering recommendations makes simulated users reach their target items in a conversational recommender system more quickly …

Combining similarity and sentiment in opinion mining for product recommendation
R Dong, MP O’Mahony, M Schaal, K McCarthy… – Journal of Intelligent …, 2016 – Springer
Skip to main content Skip to sections This service is more advanced with JavaScript available, learn more at http://activatejavascript.org …

Mobile-Based Intelligent Transportation for Bus Commuters Based on Twitter Analytics
T Mahmood, G Mujtaba, L Shuib, NZ Ali… – Frontiers of …, 2016 – ieeexplore.ieee.org
… on, 2011, pp. 1-6. [5] T. Mahmood, G. Mujtaba, and A. Venturini, “Dynamic personalization in conversational recommender systems,” Information Systems and e-Business Management, vol. 12, pp. 213-238, 2014. [6] G. Mujtaba …

Result diversification in social image retrieval: a benchmarking framework
B Ionescu, A Popescu, AL Radu, H Müller – Multimedia Tools and …, 2016 – Springer

Personality in Computational Advertising: A Benchmark.
G Roffo, A Vinciarelli – EMPIRE@ RecSys, 2016 – di.uniba.it
Page 26. Personality in Computational Advertising: A Benchmark Giorgio Roffo University of Verona Department of Computer Science Giorgio. Roffo@ univr. it Alessandro Vinciarelli University of Glasgow School of Computing Science Alessandro. Vinciarelli@ glasgow. ac …

A collaborative location based travel recommendation system through enhanced rating prediction for the group of users
L Ravi, S Vairavasundaram – Computational intelligence and …, 2016 – dl.acm.org
Page 1. Research Article A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users Logesh Ravi and Subramaniyaswamy Vairavasundaram School of Computing …

Research note—In CARSs we trust: How context-aware recommendations affect customers’ trust and other business performance measures of recommender systems
U Panniello, M Gorgoglione… – Information Systems …, 2016 – pubsonline.informs.org
Page 1. Information Systems Research Vol. 27, No. 1, March 2016, pp. 182–196 ISSN 1047-7047 (print) ISSN 1526-5536 (online) http://dx.doi.org/10.1287/isre.2015.0610 © 2016 INFORMS Research Note In CARSs We Trust: How Context-Aware …

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations
J Moody, DH Glass – ACM Transactions on Intelligent Systems and …, 2016 – dl.acm.org
Page 1. 42 A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations JENNIFER MOODY and DAVID H. GLASS, University of Ulster The primary goal of a recommender …

I should not recommend it to you even if you will like it: the ethics of recommender systems
TY Tang, P Winoto – New Review of Hypermedia and Multimedia, 2016 – Taylor & Francis
Page 1. I should not recommend it to you even if you will like it: the ethics of recommender systems TIFFANY YA TANG* and PINATA WINOTO Department of Computer Science, Wenzhou Kean University, Wenzhou, China (Received 2 August 2014; accepted 22 April 2015) …

Web Authentication using Third-Parties in Untrusted Environments
A Vapen – 2016 – books.google.com
Page 1. Linköping Studies in Science and Technology Dissertation No. 1768 Web Authentication using Third-Parties in Untrusted Environments Anna Vapen Page 2. Linköping Studies in Science and Technology Dissertations. No …

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems
M Kaminskas, D Bridge – ACM Transactions on Interactive Intelligent …, 2016 – dl.acm.org
Page 1. 2 Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems MARIUS KAMINSKAS and DEREK BRIDGE, Insight Centre for Data Analytics, University College Cork, Ireland …

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