## Recommender Dialog Systems 2016

recommender system / recommender systems

dialog system / dialog systems / dialogue system / dialogue systems

Notes:

• Negotiation dialogs
• Recommendation dialogs

Wikipedia:

References:

100 Best Recommender System Videos | Conversational Recommender Systems 2016

GeoSRS: A hybrid social recommender system for geolocated data
J Capdevila, M Arias, A Arratia – Information Systems, 2016 – Elsevier
… Reschke et al. [40] propose a recommendation dialog system built upon narrow questions from reviews, which slightly differs from the recommendation problem definition. In contrast to all these systems, our recommender system bases the whole recommendation on the …

Fusing social media cues: personality prediction from twitter and instagram
M Skowron, M Tkal?i?, B Ferwerda… – Proceedings of the 25th …, 2016 – dl.acm.org
… ABSTRACT Incorporating users’ personality traits has shown to be in- strumental in many personalized retrieval and recommender systems. … Effect of affective profile on communication patterns and affective expressions in interactions with a dialog system. …

Facilitating safe adaptation of interactive agents using interactive reinforcement learning
K Tsiakas – Companion Publication of the 21st International …, 2016 – dl.acm.org
… Learning (RL) provides an appropriate framework for interaction optimization and has been successfully applied to model the interaction management of Adaptive Dialogue Systems [12],[4], Intelligent Tutoring Systems [2],[9] and Recommender Systems [8], considering the …

Emotions and personality in adaptive e-learning systems: an affective computing perspective
OC Santos – Emotions and Personality in Personalized Services, 2016 – Springer
… Ontological approach, supervised learning. Non-specified affective tactic. Litman and Forbes-Riley, 2014 [48]. Physics Intelligent Tutoring Dialogue System (ITSPOKE) $$\text {N}=67$$. Voice. Disengagement, uncertainity (manual labelling not required). …

Interactive persuasive systems: a perspective on theory and evaluation
A Spagnolli, L Chittaro, L Gamberini – International Journal of …, 2016 – Taylor & Francis

Unsupervised user intent modeling by feature-enriched matrix factorization
YN Chen, M Sun, AI Rudnicky… – Acoustics, Speech and …, 2016 – ieeexplore.ieee.org
… Chen, William Yang Wang, and Alexander I Rud- nicky, “Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame … 23] Yehuda Koren, Robert Bell, and Chris Volinsky, “Matrix fac- torization techniques for recommender systems,” Computer, , no …

Hierarchical memory networks
S Chandar, S Ahn, H Larochelle, P Vincent… – arXiv preprint arXiv …, 2016 – arxiv.org
… have been introduced in [2] and have been so far applied to comprehension-based question answering [13, 14], large scale question answering [4] and dialogue systems [15]. … Speeding up the xbox recommender system using a euclidean transformation for inner-product spaces …

On the track of Artificial Intelligence: Learning with intelligent personal assistants
NG Canbek, ME Mutlu – Journal of Human Sciences, 2016 – j-humansciences.com
… through software agents specialized in tasks such as improving the information retrieval process, or supporting users through recommender systems. … Developed on a spoken dialogue system that uses a natural spoken language and semantic understanding techniques in an …

Learning distributed representations of sentences from unlabelled data
F Hill, K Cho, A Korhonen – arXiv preprint arXiv:1602.03483, 2016 – arxiv.org
… phrases or sentences as continuous-valued vectors. Examples include machine translation (Sutskever et al., 2014), image captioning (Mao et al., 2015) and dialogue systems (Serban et al., 2015). While it has been ob- served …

Using multiple storylines for presenting large information networks
Z Battad, M Si – International Conference on Intelligent Virtual Agents, 2016 – Springer
… In: Ricci, F., et al. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, New York (2011)CrossRef. 12. … In: Lee, GG, Kim, HK, Jeong, M., Kim, JH (eds.) Natural Language Dialog Systems and Intelligent Assistants, pp. 233–239 (2015). 15. …

Control of proclivity toward selling electricity using persuasive dialog system
K Kitagawa, K Kogiso – Control Systems (ISCS), 2016 SICE …, 2016 – ieeexplore.ieee.org
… Dialogue management for leading the conversation in persuasive dialogue systems”, IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 114–119, 2013. [7] P. Warnest, “ User evaluation of a conversational recommender system”, Proceedings of …

The Formation of User Model in Scientifi c Recommender Systems
AI Guseva, VS Kireev, PV Bochkarev… – … of Management and …, 2016 – search.proquest.com
… Guseva, et al.: The Formation of User Model in Scientic Recommender Systems. The system user can have their own tastes and preferences. … The dialogue system support interactive process. In such systems more advanced transaction models. …

Online social trust reinforced personalized recommendation
Y Cheng, J Liu, X Yu – Personal and Ubiquitous Computing, 2016 – Springer
… Simply representing the user’s profile information by a bag of words is not sufficient to capture the exact interests of the user in some scenarios, although CB recommender systems are easy to implement. … In: DELOS-NSF workshop on recommender systems. 12. …

A survey: Cyber-physical-social systems and their system-level design methodology
J Zeng, LT Yang, M Lin, H Ning, J Ma – Future Generation Computer …, 2016 – Elsevier
The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environ.

Why “blow out”? a structural analysis of the movie dialog dataset
R Searle, M Bingham-Walker – ACL 2016, 2016 – aclweb.org
… 1 Introduction There has been a recent upsurge of commercial in- terest in the development of intelligent dialogue systems to answer questions, provide personal- ized recommendations and deliver services across a range … A Sur- vey of Explanations in Recommender Systems. …

Spectral decomposition method of dialog state tracking via collective matrix factorization
J Perez – arXiv preprint arXiv:1606.05286, 2016 – arxiv.org
… In this paper, a latent decomposition type of approach is proposed in order to address this general problem of dialog system. … paradigm also for data fusion and bias by data type of modeling as successfully performed in matrix factorization based recommender systems Koren et …

A taxonomy for user models in adaptive systems: special considerations for learning environments
N Medina-Medina, L García-Cabrera – The Knowledge Engineering …, 2016 – cambridge.org
… Among these, the taxonomy of beliefs and goals for UMs in dialog systems proposed in Kobsa (1989) should be noted. … An example is similarity-usage based retrieval (SUBR) (Khemani et al., 2006), a recommender system that can learn from its experience with the user. …

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond
V Dimitrova, P Brna – International Journal of Artificial Intelligence in …, 2016 – Springer
… The above challenges were addressed by relying on existing research in dialogue systems, external representations and communication languages, and logical … (2009) to allow users to understand how a user-adapted social recommender system makes adaptation decisions …

Fostering parent–child dialog through automated discussion suggestions
A Boteanu, S Chernova, D Nunez… – User Modeling and User …, 2016 – Springer
… Keywords. Context-aware computingUser modelsDialog analysisRecommender system. … More broadly, recommender systems have been applied in a variety of domains, such as for restaurant recommendations (Boteanu and Chernova 2013b), music suggestions (McFee et al. …

Modeling a Dialog System for Movie Recommendations Based on The Movie Database
KS Nenova – isl.anthropomatik.kit.edu
… The ELIZA system mentioned in Chapter 2.1.2 is an important example of a Social Dialog System and one of the rst of … The hybrid RSs are described in [2, page 380] as “any recommender system that combines multiple recommendation techniques together to produce its output …

Minimal Interaction Content Discovery in Recommender Systems
B Kveton, S Berkovsky – ACM Transactions on Interactive Intelligent …, 2016 – dl.acm.org
Page 1. 15 Minimal Interaction Content Discovery in Recommender Systems BRANISLAV KVETON, Adobe Research SHLOMO BERKOVSKY, CSIRO … 1. INTRODUCTION Recommender systems are used in a variety of eCommerce sites and social networks. …

Minimal Interaction Content Discovery in Recommender Systems
S BERKOVSKY – pdfs.semanticscholar.org
Page 1. 15 Minimal Interaction Content Discovery in Recommender Systems BRANISLAV KVETON, Adobe Research SHLOMO BERKOVSKY, CSIRO … 1. INTRODUCTION Recommender systems are used in a variety of eCommerce sites and social networks. …

Personalized news event retrieval for small talk in social dialog systems
L Bechberger, M Schmidt, A Waibel… – … ; 12. ITG Symposium; …, 2016 – ieeexplore.ieee.org
… The NewsTeller system returns a short summary of the selected news event to the social dialog system. … each event was labeled by only one annotator, we could not define features based on ratings of other users (as it is done in “collaborative filtering” recommender systems). …

Related Word Recommendation Mechanism for Speech Dialogue System
Y Ishida, T Uchiya, K Yamamoto… – … (NBiS), 2016 19th …, 2016 – ieeexplore.ieee.org
Related Word Recommendation Mechanism for Speech Dialogue System Yuto Ishida ? , Takahiro Uchiya ? , Kouhei Yamamoto ? , Daisuke Yamamoto ? , Ryota Nishimura † , Ichi Takumi ? … Abstract—In recent years, speech dialogue systems have de- veloped remarkably. …

Quote Recommendation in Dialogue using Deep Neural Network
H Lee, Y Ahn, H Lee, S Ha, S Lee – … of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… In this paper, we introduce a task of recommending quotes which are suitable for given dialogue context and we present a deep learning recommender system which combines recurrent … On-line policy optimisation of bayesian spoken dialogue systems via human interaction. …

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
… in persuasive dialogue systems, IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 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 …

Conversational Recommendation System with Unsupervised Learning
Y Sun, Y Zhang, Y Chen, R Jin – … Conference on Recommender Systems, 2016 – dl.acm.org
… hand labeled training data. CCS Concepts •Information systems ? Recommender systems; Keywords Dialogue systems, recommendation systems, personal assis- tant, chat bot 1. INTRODUCTION Recommendation systems …

Psychological reactance in HCI: a method towards improving acceptance of devices and services
P Ehrenbrink, S Hillmann, B Weiss… – Proceedings of the 28th …, 2016 – dl.acm.org
… Furthermore, we test our hypothesis that interaction with a self-adaptive spoken dialogue system (SDS) (system behaviour changes autonomous … psychological reactance can be moderated by the amount of social cues of an embodied virtual agent-based recommender system. …

Modeling user’s decision process through gaze behavior
K Shimonishi – Proceedings of the 18th ACM International Conference …, 2016 – dl.acm.org
… Therefore, de- signing recommender systems that support users by interac- tively sharpening users’ focus of decision making (eg, con- verging the number of alternatives) has been an important research topic [4, 10, 13, 23]. … [10] proposed a spoken dialogue system based on …

Evaluation Method for an Adaptive Web Interface: GOMS Model
R Rim, MM Amin, M Adel, A Mohamed – International Conference on …, 2016 – Springer
… Rev. 7, 157–184 (1993)CrossRefGoogle Scholar. 16. Middleton, S., Shadbolt, N., De Roure, D.: Ontological user profiling in recommender systems. ACM Trans. Inf. … Rich, E.: Stereotypes and user modeling. In: Kobsa, A., Wahlster, W. (eds.) User Models in Dialog Systems, Chap. …

UNSUPERVISED USER INTENT MODELING BY FEATURE-ENRICHED MATRIX FACTORIZATION
YNCMS Alexander, IRA Gershman – cs.cmu.edu
… Chen, William Yang Wang, and Alexander I Rud- nicky, “Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame … 23] Yehuda Koren, Robert Bell, and Chris Volinsky, “Matrix fac- torization techniques for recommender systems,” Computer, , no …

Evaluation of In-Car SDS Notification Concepts for Incoming Proactive Events
H Hofmann, M Hermanutz, V Tobisch, U Ehrlich… – Situated Dialog in …, 2016 – Springer
… Results show that the proactive recommender system is perceived as helpful and does not distract from driving. … A speech dialog system (SDS) prototype supported by a graphical user interface (GUI) employing the designed notification concepts has been developed for German …

1 39th German Conference on Artificial Intelligence/OGAI-Tagung 2016
CR Fichte, S Woltran – Springer
… Recommender systems. • Robotics. … In the first keynote, Roberto Pieraccini gave a historical insight on the evolution of dialog systems starting from the simple speech recognition, which was limited to under- stand the numbers one to ten only. He then described the …

Artificial Intelligence: Uses and Misuses
H Alzahrani – Global Journal of Computer Science and …, 2016 – computerresearch.org
… Such dialog systems are extremely useful for people who are less tech savvy as they can just order their phone to do things for them rather than … g) Recommender System Recommender Systems are now used by all digital marketing vendors and even blogs and social websites. …

Humans and Machines in the Evolution of AI in Korea
BT Zhang – AI Magazine, 2016 – go.galegroup.com
… A large number of intelligent agents were developed for information filtering, comparison shopping, recommender systems, and smart user … The goal is to develop natural language dialogue systems for knowledge communications between humans and machines in specific …

Automatically Classifying Self-Rated Personality Scores from Speech.
G An, SI Levitan, R Levitan, A Rosenberg… – …, 2016 – venus.cs.qc.edu
… work we experiment with ways to automati- cally identifying the NEO-FFI Big Five personality traits from speech, which will be useful for applications such as dialogue systems. … 11] R. Hu and P. Pu, “A study on user perception of personality-based recommender systems,” in User …

The Goal Behind the Action: Toward Goal-Aware Systems and Applications
D Papadimitriou, G Koutrika, J Mylopoulos… – ACM Transactions on …, 2016 – dl.acm.org
Page 1. 23 The Goal Behind the Action: Toward Goal-Aware Systems and Applications DIMITRA PAPADIMITRIOU, University of Trento, Italy GEORGIA KOUTRIKA, HP Labs, USA JOHN MYLOPOULOS and YANNIS VELEGRAKIS, University of Trento, Italy …

Highly Pertinent Algorithm for the Market of Business Intelligence, Context and Native Advertising
AI Guseva, VS Kireev… – International Journal of …, 2016 – search.proquest.com
… subject. The recommender system receives information about the user, rst, at his registration in the scientic system. … actions. In dialogue systems, ie, the systems supporting interactive process, more advanced models of transaction. …

Towards the Experimental Studies of a Web-based Platform for Eco-Tourism
D Halvatzaras, K Kabassi – ijres.org
… 2003, Loh et al. 2003, Rabanser and Ricci 2005). As a result, recommender systems have been successfully used in travel and tourism (Adomavicius & Tuzhilin 2005). … 2003, Niaraki & Kim 2009). Some recommender systems only focus on some aspect of the holidays. …

User Interface Personalization in News Apps.
M Constantinides, J Dowell – UMAP (Extended Proceedings), 2016 – ceur-ws.org
… Public Report, 2014. [6] Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state- of-the-art and possible extensions. … & Wahlster, W. User Models in Dialog Systems. A. Kobsa, & W. Wahlster (Eds.) 2012. …

Comparative studies of AIML
Y Wei, X Zhu, B Sun, B Sun – Systems and Informatics (ICSAI) …, 2016 – ieeexplore.ieee.org
… Three experimental systems have been constructed (one is a pure natural language dialogue system, one is a related domain knowledge system and one is a combination of a dialogue and … [20] Xue Weilian, Wang Yunhui, “An E-commerce Recommender System Based on …

Conquering an Exo-planet Through the use of a Virtual Role Playing Game Assisted by an Emotionally Intelligent Pedagogical Agent
A Terracina, F Fabiani, LS Ferro… – … on Games Based …, 2016 – search.proquest.com
… Virtual Learning Environments (VLEs) integrate several educational resources including multimedia learning material, communication tools, and recommender systems, among others. … We start from the result found by Mori to implement our dialogue system. …

Navigating the Spoken Wikipedia
M Rohde, T Baumann – Proc. SLPAT …, 2016 – nats-www.informatik.uni-hamburg.de
… Smolibocki, and M. Stede, “Eval- uation of information structure in speech synthe- sis: The case of product recommender systems,” in Speech … Black, K. Sagae, and DR Traum, “Practical evaluation of human and synthesized speech for virtual human dialogue systems.” in LREC …

Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality
DBLCL Anne, L Ligozat, SRP Zweigenbaum – lrec-conf.org
… It is useful for many NLP applications such as information retrieval (Liu, 2009), machine transla- tion (Duh and Kirchhoff, 2008), or recommender systems (Lv et al., 2011). … De- scription of the PatientGenesys dialogue system. In Proc. of 16th SIGDIAL, pages 438–440. …

Towards building a review recommendation system that trains novices by leveraging the actions of experts
S Khanal – 2016 – digitalcommons.unl.edu
… experts. Item recommenders, as the name implies, recommend items to users. These are the most popular type of recommender systems especially used by e-commerce websites. These systems analyze users’ purchase history and recommend items that are most …

” Is There Anything Else I Can Help You With?” Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
THK Huang, WS Lasecki, A Azaria… – Fourth AAAI Conference on …, 2016 – aaai.org
Page 1. “Is There Anything Else I Can Help You With?” Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent Ting-Hao (Kenneth) Huang,1 Walter S. Lasecki,2 Amos Azaria,1,3 Jeffrey P. Bigham1 1 Carnegie Mellon University, Pittsburgh, PA, USA. …

Strategic advice provision in repeated human-agent interactions
A Azaria, Y Gal, S Kraus, CV Goldman – Autonomous Agents and Multi …, 2016 – Springer
… data. Recommender systems [10] advise users to take certain actions, usually from a large set of actions. Users … user. Past work in user-modeling have generated advice in dialogue systems or collaborative office assistants [23]. These …

Technology support for discussion based learning: From computer supported collaborative learning to the future of massive open online courses
CP Rosé, O Ferschke – International Journal of Artificial Intelligence in …, 2016 – Springer
… Beginning in the mid-90s, this interest initially focused mainly on the area of tutorial dialogue systems to support individual learning (Evens … The use of social recommender systems (such as Quick Helper) and group collabora- tion tools (such as Bazaar) are expected to lead to …

Goal-aware data management for retrieval and recommendations
D Papadimitriou – … 2016 IEEE 32nd International Conference on, 2016 – ieeexplore.ieee.org
… actions) [8], to perform interface adaptations [9], to promote or facilitate the performance of certain actions by exposing the actor to information regarding actions, eg, action preconditions, that are involved in the operationalization of the goal, eg, dialogue systems [10], to …

DeepSoft: A vision for a deep model of software
HK Dam, T Tran, J Grundy, A Ghose – Proceedings of the 2016 24th ACM …, 2016 – dl.acm.org
… While much work has been done on recommender systems, such as for APIs, this has mostly relied on manual feature identification and … Given the recent successes in NLP [5] (machine translation, question answering, and dialog systems) and vi- sion [4] (image/video captioning …

Music Predictions Using Deep Learning. Could LSTM Networks be the New Standard for Collaborative Filtering?
E Keski-Seppälä, M Snellman – 2016 – diva-portal.org
… Abstract Predicting the product a customer would like to buy is an increasingly important field of study and there are several different recommender system models that are used to make recommendations for users. … 2 Background 2.1 Recommender Systems 2.1.1 Introduction …

K Nazemi – Adaptive Semantics Visualization, 2016 – Springer
… (KG Saur, München, 2004). 32. A. Neumann, Recommender systems for scientific and technical information providers. … 948–960. 80. W. Wahlster, A. Kobsa, in User Models in Dialog Systems, Symbolic Computation, ed. by A. Kobsa, W. Wahlster (Springer, Berlin, 1989), pp. 4–34. …

Question answering in conversations: Query refinement using contextual and semantic information
M Habibi, P Mahdabi, A Popescu-Belis – Data & Knowledge Engineering, 2016 – Elsevier
This paper introduces a query refinement method applied to questions asked by users to a system during a meeting or a conversation that they have with other use.

A survey on data-driven approaches in educational games
D Hooshyar, C Lee, H Lim – Science in Information Technology …, 2016 – ieeexplore.ieee.org
… Designing and evaluating an adaptive spoken dialogue system”. User Modeling and User-Adapted Interaction, 12(3),111-137, 2002. … [29] N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe, and L. Schmidt- Thieme, “Recommender system for predicting student performance”. …

Gaussian Attention Model and Its Application to Knowledgebase Embedding and Question Answering
L Zhang, J Winn, R Tomioka – arXiv preprint arXiv:1611.02266, 2016 – arxiv.org
Page 1. Under review as a conference paper at ICLR 2017 GAUSSIAN ATTENTION MODEL AND ITS APPLICATION TO KNOWLEDGE BASE EMBEDDING AND QUESTION ANSWERING Liwen Zhang Department of Computer …

A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition
CP Latha, M Priya – APTIKOM Journal on Computer Science …, 2016 – jurnal.aptikom.or.id
… 91 that there is an overlapping of the input regions that gives a clear representation of the original input image. The process is repeated for all the layers. CNNs are mostly used in video and image recognition, natural language processing and recommender systems. …

Evaluation of a trust-modulated argumentation-based interactive decision-making tool
EI Sklar, S Parsons, Z Li, J Salvit, S Perumal… – Autonomous Agents and …, 2016 – Springer

The effect of personalization provider characteristics on privacy attitudes and behaviors: An Elaboration Likelihood Model approach
A Kobsa, H Cho, BP Knijnenburg – Journal of the Association …, 2016 – Wiley Online Library
By continuing to browse this site you agree to us using cookies as described in About Cookies. Remove maintenance message. …

An empirical investigation of word class-based features for natural language understanding
A Celikyilmaz, R Sarikaya, M Jeong… – IEEE/ACM Transactions …, 2016 – dl.acm.org
… The first set is internally collected multimedia data from live deployment scenarios of a spoken dialog system designed for entertainment search for Xbox One game console. … Generic search utterances are sent by the dialog system to the Bing search engine. …

Data Formats for Emotion and Personality
MASN Nunes, J Granatyr – meninasnacomputacao.com.br
… making focused in Educational Systems. On the oth- er hand, Nunes [2008] modeled personality aspects and applied it in recommender systems in order to improve systems personalization. These works inspired others scientists …

Modeling citizens’ urban time-use using adaptive hypermedia surveys to obtain an urban planning, citizen-centric, methodological reinvention
ML Marsal-Llacuna, R Fabregat-Gesa – Time & Society, 2016 – journals.sagepub.com

O Cailloux, U Endriss – … of the 2016 International Conference on …, 2016 – dl.acm.org
… in recent years, initially sparked by the relevance of the theory to AI applications in areas such as recommender systems, multiagent systems … System: Consider election 1, involving only voter 1 (see also Figure 1). Do you agree that , enjoying unanimous support, should win this …

An analysis of student model portability
BV Aguirre, JAR Uresti, B Du Boulay – International Journal of Artificial …, 2016 – Springer
… Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A … personalization. Recommender Systems (RSs) (Pu et al. 2012; Bobadilla et al. …

Inferring capabilities of intelligent agents from their external traits
BP Knijnenburg, MC Willemsen – ACM Transactions on Interactive …, 2016 – dl.acm.org
… 1. INTRODUCTION Advice-giving systems have been around for several decades in the form of intelligent tutoring systems [Sleeman and Brown 1982], expert systems [Carroll and McKendree 1987], and recommender systems [Schafer et al. 1999]. …

Role of Learning Analytics in Enhancing Teaching and Learning
A Manzoor – Developing Effective Educational Experiences …, 2016 – books.google.com
… This data represents the interactions between learners and the learning system. User knowledge modeling is used to build adaptive hypermedia, recommender systems, expert systems, and intelligent tutoring systems. Intelligent …

Innovative techniques for the implementation of Adaptive Mobile Learning using the Semantic Web
SE Hamada – 2016 – search.proquest.com
… Utilizing Social Semantic Web (SSW) and Recommender System (RS) advances the gPLEc coordinates teachers PLE-based learning skills and learners’ social web … Interactive alignment of human and computer has been handled by an ISU-based Dialog System Architecture. …

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation
A Bilgin, H Hagras, J van Helvert… – IEEE Transactions on …, 2016 – ieeexplore.ieee.org
… As another perspective, using recipe recommendation experiments, Forbes and Zhu [81] investigated content-boosted matrix factorization for recommender systems. However, these studies have not considered user conditions that affect food se- lection. …

Persuasive strategies for encouraging social interaction for older adults
JP Vargheese, S Sripada, J Masthoff… – International Journal of …, 2016 – Taylor & Francis

Adapting progress feedback and emotional support to learner personality
M Dennis, J Masthoff, C Mellish – International Journal of Artificial …, 2016 – Springer
Page 1. Int J Artif Intell Educ (2016) 26:877–931 DOI 10.1007/s40593-015-0059- 7 ARTICLE Adapting Progress Feedback and Emotional Support to Learner Personality Matt Dennis1 ·Judith Masthoff1 ·Chris Mellish1 Published …

IMPROVING KNOWLEDGE BASE POPULATION WITH INFORMATION EXTRACTION
X Li – 2016 – pdfs.semanticscholar.org
Page 1. IMPROVING KNOWLEDGE BASE POPULATION WITH INFORMATION EXTRACTION by Xiang Li A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science New York University May, 2016 …

Multi-behavioral Sequential Prediction for Collaborative Filtering
Q Liu, S Wu, L Wang – arXiv preprint arXiv:1608.07102, 2016 – pdfs.semanticscholar.org
… log-bilinear. I. INTRODUCTION Nowadays, Collaborative Filtering (CF) plays an important role in a large number of applications, eg, recommender systems, information retrieval and social network analysis. Conventional CF …

Designing Human-Centered Collective Intelligence
ID Addo – 2016 – search.proquest.com
… The discerned affective state is used in recommender systems among others to support content personalization. … 22. D. Motivational Interviewing in CI dialogue systems ….. 23. E. Modeling Privacy Preservation in CI ….. 24. …

CEST: City Event Summarization using Twitter
D Mallela – 2016 – scholarworks.boisestate.edu
Page 1. Boise State University ScholarWorks Computer Science Graduate Projects and Theses Department of Computer Science 5-1-2016 CEST: City Event Summarization using Twitter Deepa Mallela Boise State University Page 2. CEST: …

The Internet of Things supporting the Cultural Heritage domain: analysis, design and implementation of a smart framework enhancing the smartness of cultural spaces
F Piccialli – 2016 – fedoa.unina.it
Page 1. Universit`a degli Studi di Napoli Federico II Dipartimento di Matematica e Applicazioni “Renato Caccioppoli” Ph.D. Thesis in Scienze Computazionali e Informatiche – XXVIII Ciclo The Internet of Things supporting the Cultural Heritage domain: analysis, design and …

Sub-Saharan African languages: from speech fundamentals to applications
M Adda-Decker, L Besacier, M Davel, L Hyman… – 2016 – isca-speech.org
… discourse and dialogue for both ACL and ISCA. SIGDIAL 2016 will be co-located with INTERSPEECH 2016 as a satellite event, and also with YRRSDS 2016, the Young Researchers’ Roundtable on Spoken Dialog Systems. …

Linguistic Linked Open Data: 12th EUROLAN 2015 Summer School and RUMOUR 2015 Workshop, Sibiu, Romania, July 13-25, 2015, Revised Selected …
D Trandab??, D Gîfu – 2016 – books.google.com
Page 1. Diana Trandab?? Daniela Gîfu (Eds.) Communications in Computer and Information Science 588 Linguistic Linked Open Data 12th EUROLAN 2015 Summer School and RUMOUR 2015 Workshop Sibiu, Romania, July 13–25, 2015 Revised Selected Papers 123 Page 2. …

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. …

Mobile appointment reminders in patient-centered care: Design and evaluation
Y Wang – 2016 – search.proquest.com
Mobile appointment reminders in patient-centered care: Design and evaluation. Abstract. Reminder systems have great potential to enhance healthcare outcome if it can facilitate collaborative appointment management with …

Large-scale affective computing for visual multimedia
B Jou – 2016 – search.proquest.com
… this value such as view, like and share counts are undoubtedly valuable metrics; however, they fall short in many scenarios, such as in metric ination spam [Cha et al., 2007; Benevenuto et al., 2009], the cold start problem in personalization and recommender systems [Schein et …

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