An Analysis of Emotion and User Behavior for Context-aware Recommendation Systems using Pre-filtering and Tensor Factorization Techniques

Piumi Ishanka; Takashi Yukawa

An Analysis of Emotion and User Behavior for Context-aware Recommendation Systems using Pre-filtering and Tensor Factorization Techniques

Piumi Ishanka * §

Contact

Takashi Yukawa

Contact

Nagaoka University of Technology

An Analysis of Emotion and User Behavior for Context-aware Recommendation Systems using Pre-filtering and Tensor Factorization Techniques

Article Fingerprint

ReserarchID

CST099TF

An Analysis of Emotion and User Behavior for Context-aware Recommendation Systems using Pre-filtering and Tensor Factorization Techniques Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

Abstract

The emotion-based context-aware recommendation systems have been widely adapted by a wide variety of recommendation domains despite the fact only a few studies have been analyzed in tourist destination recommendations. To utilize the concept of user emotion and incorporate it into the recommendation process along with user behavior, we proposed a travel destination recommendation system. Also, we compare and clarify the effectiveness of using emotion and user behavior in the recommendation process by suggesting a framework based on two techniques: filtering and contextual modeling. For the filtering based approach, we used Prefiltering, and for the contextual modeling, we employed Tensor Factorization. Both these approaches performed excellently with the selected contexts in the proposed framework, and the results of the Tensor Factorization approach proved to be highly effective in tourist destination recommendation compared to other Pre-filtering.

References

46 Cites in Article

Reference Format

P Resnick,H Varian (1997). Recommender systems.
Tkalčič (2016). Emotions and Personality in Personalized Services.
L Baltrunas,X Amatriain (2009). Towards time-dependant recommendation based on implicit feedback.
M Domingues,A Jorge,C Soares (2011). Using contextual information as virtual items on top-n recommender systems.
M Böhmer,G Bauer,A Krüger (2010). Exploring the design space of contextaware recommender systems that suggest mobile applications.
S Lombardi,S Anand,M Gorgoglione (2009). Context and customer behaviour in recommendation.
Y Zheng,B Mobasher,R Burke (2013). The Role of Emotions in Context-aware Recommendation.
Francesco Ricci,Lior Rokach,Bracha Shapira (2011). Introduction to Recommender Systems Handbook.
A Dey (2001). Understanding and using context.
Pedro Campos,Ignacio Fernández-Tobías,Iván Cantador,Fernando Díez (2013). Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches.
F Lemos,R Carmo,W Viana,R Andrade (2012). Towards a context-aware photo recommender system.
F Marinho,R Andrade,C Werner,W Viana,M Maia,L Rocha,. Aguiar,S (2013). MobiLine: A Nested Software Product Line for the domain of mobile and context-aware applications.
M Tkalcic,A Kosir,J Tasic (2011). Affective recommender systems: the role of emotions in recommender systems.
Gustavo Gonzalez,Josep De La Rosa,Miquel Montaner,Sonia Delfin (2007). Embedding Emotional Context in Recommender Systems.
Rui Cai,Chao Zhang,Chong Wang,Lei Zhang,Wei-Ying Ma (2007). MusicSense.
B Han,S Rho,S Jun,E Hwang (2010). Music emotion classification and context-based music recommendation.
Man-Kwan Shan,Fang-Fei Kuo,Meng-Fen Chiang,Suh-Yin Lee (2009). Emotion-based music recommendation by affinity discovery from film music.
Karzan Wakil,Rebwar Bakhtyar,Karwan Ali,Kozhin Alaadin (2015). Improving Web Movie Recommender System Based on Emotions.
A Ho,I Menezes,Y Tagmouti (2006). Emrs: Emotion-based movie recommender system.
Y Huang,L Bian (2009). A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet.
L Castillo,E Armengol,E Onaindía,L Sebastiá,J González-Boticario,A Rodríguez,. Borrajo,D (2008). SAMAP: An user-oriented adaptive system for planning tourist visits.
Y Zheng,R Burke,B Mobasher (2012). Differential context relaxation for context-aware travel recommendation.
S Mohamed,T Soliman,A Sewisy (2016). A context-aware recommender system for personalized places in mobile applications.
M Barranco,J Noguera,J Castro,L Martínez (2012). A context-aware mobile recommender system based on location and trajectory.
Damianos Gavalas,Michael Kenteris (2011). A web-based pervasive recommendation system for mobile tourist guides.
José Noguera,Manuel Barranco,Rafael Segura,Luis Martínez (2012). A mobile 3D-GIS hybrid recommender system for tourism.
Soe Yuan,Chun-Ya Yang (2017). Service recommender system based on emotional features and social interactions.
J Borràs,A Moreno,A Valls (2014). Intelligent tourism recommender systems: A survey.
Gediminas Adomavicius,Alexander Tuzhilin (2015). Context-Aware Recommender Systems.
J Herlocker,J Konstan,L Ter-Veen,J Riedl (2004). Evaluating collaborative filtering recommender systems.
J Breese,D Heckerman,C Kadie (1998). Empirical analysis of predictive algorithms for collaborative filtering.
Joseph Konstan,Bradley Miller,David Maltz,Jonathan Herlocker,Lee Gordon,John Riedl (1997). GroupLens.
Pierre Comon (2014). Tensors : A brief introduction.
Lars Grasedyck,Daniel Kressner,Christine Tobler (2013). A literature survey of low‐rank tensor approximation techniques.
Tamara Kolda,Brett Bader (2009). Tensor Decompositions and Applications.
R Plutchik,H Kellerman (1980). Emotion: Theory, Research and Experience. Volume 1. <i>Theories of Emotion</i>. Edited by R. Plutchik and H. Kellerman. (Pp. 399; illustrated; £19.00.) Academic Press: London. 1980..
Songjie Gong (2010). A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering.
Xiaoyuan Su,Taghi Khoshgoftaar (2009). A Survey of Collaborative Filtering Techniques.
T Dunning (1993). Accurate methods for the statistics of surprise and coincidence.
Meng-Yen Hsieh,Wen-Kuang Chou,Kuan-Ching Li (2017). Building a mobile movie recommendation service by user rating and APP usage with linked data on Hadoop.
Manos Papagelis,Dimitris Plexousakis (2005). Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents.
S Schelter,S Owen (2012). Collaborative filtering with apache mahout.
Yong Zheng,Bamshad Mobasher,Robin Burke (2015). CARSKit: A Java-Based Context-Aware Recommendation Engine.
G Shani,A Gunawardana (2011). Evaluating recommendation systems.
F Isinkaye,Y Folajimi,B Ojokoh (2015). Recommendation systems: Principles, methods and evaluation.
U Ishanka,T Yukawa (2017). The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Piumi Ishanka, Takashi Yukawa. 2018. "An Analysis of Emotion and User Behavior for Context-aware Recommendation Systems using Pre-filtering and Tensor Factorization Techniques". Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 18 (GJCST Volume 18 Issue D1).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 18 Issue D1

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-D Classification J.4

I.m

Submission ReceivedDecember 13, 2017
Peer Review Double Blind
Handling Editor
Accepted January 1, 2018
Published January 15, 2018

Version of record

v1.2

Issue date

April 13, 2018

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 188

Country: Japan

Subject: Global Journal of Computer Science and Technology

Authors: Piumi Ishanka, Takashi Yukawa, Takashi Yukawa (PhD/Dr. count: 0)

View Count (all-time): 483

Total Views (Real + Logic): 2390

Total Downloads (simulated): 155

Publish Date: 2018 01, Mon

Monthly Totals (Real + Logic):

Month 1: 34 views
Month 2: 58 views
Month 3: 40 views
Month 4: 65 views
Month 5: 56 views
Month 6: 38 views
Month 7: 30 views
Month 8: 32 views
Month 9: 23 views
Month 10: 49 views
Month 11: 48 views
Month 12: 35 views
Month 13: 41 views
Month 14: 28 views
Month 15: 40 views
Month 16: 27 views
Month 17: 32 views
Month 18: 25 views
Month 19: 37 views
Month 20: 20 views
Month 21: 32 views
Month 22: 35 views
Month 23: 35 views
Month 24: 25 views
Month 25: 35 views
Month 26: 19 views
Month 27: 26 views
Month 28: 32 views
Month 29: 14 views
Month 30: 14 views
Month 31: 36 views
Month 32: 44 views
Month 33: 13 views
Month 34: 40 views
Month 35: 35 views
Month 36: 21 views
Month 37: 32 views
Month 38: 14 views
Month 39: 24 views
Month 40: 25 views
Month 41: 14 views
Month 42: 27 views
Month 43: 24 views
Month 44: 12 views
Month 45: 21 views
Month 46: 43 views
Month 47: 39 views
Month 48: 46 views
Month 49: 39 views
Month 50: 24 views
Month 51: 33 views
Month 52: 27 views
Month 53: 23 views
Month 54: 26 views
Month 55: 16 views
Month 56: 21 views
Month 57: 33 views
Month 58: 22 views
Month 59: 30 views
Month 60: 36 views
Month 61: 36 views
Month 62: 30 views
Month 63: 22 views
Month 64: 50 views
Month 65: 31 views
Month 66: 32 views
Month 67: 27 views
Month 68: 15 views
Month 69: 25 views
Month 70: 42 views
Month 71: 33 views
Month 72: 17 views
Month 73: 33 views
Month 74: 22 views
Month 75: 40 views
Month 76: 29 views
Month 77: 23 views
Month 78: 31 views
Month 79: 39 views
Month 80: 13 views
Month 81: 20 views
Month 82: 30 views
Month 83: 18 views
Month 84: 26 views
Month 85: 29 views
Month 86: 37 views
Month 87: 26 views
Month 88: 31 views
Month 89: 25 views
Month 90: 36 views
Month 91: 37 views
Month 92: 19 views
Month 93: 38 views
Month 94: 34 views
Month 95: 27 views
Month 96: 65 views
Month 97: 41 views
Month 98: 26 views
Month 99: 26 views
Month 100: 33 views
Month 101: 24 views
Month 102: 38 views
Month 103: 55 views
Month 104: 51 views
Month 105: 35 views

Total Views: 2.4K

Total Downloads: 155

All Trends