Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 4/2012

01-12-2012 | Original Article

A classifier ensemble approach to the TV-viewer profile adaptation problem

Authors: Ioannis T. Christou, George Gekas, Anna Kyrikou

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2012

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

For the past few years the digital video broadcasting (DVB) and Internet TV (IP-TV) market has experienced a significant growth throughout the world. As a result of this trend, many broadcasters offer such services, and many homes now feature a set-top box in their living rooms providing them with a myriad of digital TV channels. This plethora of choices dictates the need for a system that can autonomously monitor a user’s choices on TV and be able to predict what content the user is likely to appreciate. We describe a system called CPACE (continuous profile adaptation via classifier ensembles) that continuously monitors user choices, and using machine learning techniques, it classifies not-seen-before content from an electronic program guide (EPG) and suggests to the user content that he/she is likely to be interested in viewing. Since we use no collaborative filtering at all, our architecture is fundamentally different from other commercially successful approaches such as Ti-Vo or the winners of the Netflix Prize that utilize collaborative filtering techniques that necessarily suffer from privacy related issues. We use time-windows combined with online learning to adapt to changing user behavior and classifier ensemble techniques to enhance classification accuracy. As a result, the system after a short training period is capable of classifying new broadcasted content with very high accuracy reaching up to 86%. The system has been shown to exhibit very high precision in its recommendations in a real-world installation where it usually achieves rates of more than 90%, and for a significant fraction of the user population it achieves 100% correct recommendations.
previous article A discernibility matrix for the topological reduction
next article Hybrid heuristics for the single machine scheduling problem with quadratic earliness and tardiness costs

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transact Inf Syst 23(1):103–145CrossRef
2.
Ali K, van Stam W (2004) TiVo: making show recommendations using a distributed collaborative filtering architecture. In: Proceedings of the ACM Conference on Knowledge Discovery in Databases (KDD 2004)
3.
Ardissono L, Gena C, Torasso P, Bellifemine F, Difino A, Negro B (2004) User modeling and recommendation techniques for personalized electronic program guides. In: Ardissono L et al (eds) Personalized digital television. Kluwer Academic Publishers, Amsterdam, pp 3–26
4.
Avancini H, Candela L, Straccia U (2007) Recommenders in a personalized, collaborative digital library environment. J Intell Inf Syst 28:253–283CrossRef
5.
Baudisch P (1997) Designing an evolving internet TV program guide. In: Proceedings of the human–computer interaction consortium winter workshop
6.
Belkin NJ, Croft WB (1992) Information filtering and information retrieval: two sides of the same coin? Commun ACM 35:12CrossRef
7.
Biggio B, Fumera G, Roli F (2010) Multiple classifier systems for robust classifier design in adversarial environments. Int J Mach Learn Cybernetics. 1(1–4):27–41CrossRef
8.
Billsus D, Pazzani M (2000) User modeling for adaptive news access. User Model User Adap Inter 10(2–3):147–180CrossRef
9.
Bonarini A (1997) Anytime learning and adaptation of structured fuzzy behaviors. Adapt Behav 5(3–4):281–315 CrossRef
10.
Breiman L (1994) Bagging predictors. Technical Report 421, Department of Statistics, University of California, Berkeley
11.
Brin S, Page L (1998) The anatomy of a large-scale hyper-textual web search engine. In: Proceedings of the 7th international world wide web conference
12.
Buczak AL, Zimmerman J, Kurapati K (2002) Personalization: improving ease-of-use, trust and accuracy of a TV-show recommender. In: Proceedings of the AH’2002 workshop on personalization in future TV
13.
Bugajska MD, Schultz AC (2003) Anytime coevolution of form and function. In: Proceedings of the CEC-2003 congress on evolutionary computation
14.
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapt Interact 12(4):331–370MATHCrossRef
15.
Burke R (2007) Hybrid web recommender systems. Lect Notes Comput Sci 4321:377–408CrossRef
16.
Chakrabarti S (2003) Mining the web: discovering knowledge from hypertext data. Morgan Kaufmann Publishers, San Francisco
17.
Cohen WW (1995) Fast effective rule induction. In: Proceedings of the twelfth international conference on machine learning
18.
Cohen WW, Singer Y (1999) A simple, fast and effective rule learner. In: Proceedings of AAAI
19.
Duda, RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, NY
20.
Ehrmantraut M, Harder T, Wittig H, Steinmetz R (1996) The personal electronic program guide; towards the pre-selection of individual TV programs. In: Proceedings of the 1996 ACM conference on information and knowledge management
21.
Ekstrand MD, Ludwig M, Konstan JA, Riedl JT (2011) Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In: Proceedings of the 2011 ACM conference on recommender systems
22.
Fung GM, Mangasarian OL (2005) Multicategory proximal support vector machine classifiers. Mach Learn 59:1–2CrossRef
23.
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139MathSciNetMATHCrossRef
24.
25.
Grefenstette JL, Ramsey CL (1992) An approach to anytime learning. In: Proceedings of the Ninth international machine learning workshop
26.
Jahrer M, Toscher A, Legenstein R (2010) Combining predictions for accurate recommender systems. In: Proceedings of ACM conference on knowledge discovery in databases, Washington DC, USA, July 25-28, 2010
27.
Janikow C (1991) Inductive learning of decision rules from attribute-based examples: a knowledge-intensive genetic algorithm approach. Technical report TR91-030, Department of Computer Science, University of North Carolina at Chapel Hill, USA
28.
Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Transact Pattern Anal Mach Intell 20(3):226–239CrossRef
29.
Klinkenberg R (1996) Rule set quality measures for inductive learning algorithms. Master’s Thesis, Department of Computer Science, University of Missouri-Rolla, USA
30.
Kobsa A (2007) Privacy-enhanced personalization. Communications of the ACM 50(8):24–33CrossRef
31.
Kohavi R (1995) The power of decision tables. In: Proceedings of the 8th European conference on machine learning, pp. 174–189
32.
33.
Kuncheva L, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51:181–207MATHCrossRef
34.
Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 95(1–2):161–205CrossRef
35.
36.
MAES P (1994) Agents that reduce work and information overload. Commun ACM 37(7):31–40
37.
Maon S, Jiao LC, Xiong L, Gou S (2011) Greedy optimization classifiers ensemble based on diversity. Pattern Recogn 44:1245–1261CrossRef
38.
Mena E, Illarramendi A, Royo JA, Gonni A (2006) A software retrieval service based on adaptive knowledge-driven agents for wireless environments. ACM Transact Auton Adapt Syst 1(1):67–90CrossRef
39.
Michalski R (1983) A theory and methodology of inductive learning. In: Michalski R, Carbonell J, Mitchell T (eds) Machine learning: an artificial intelligence approach, Tioga Publishing, Palo-Alto
40.
Muhlbaier M, Topalis A, Polikar R (2004) Learn++.MT: a new approach to incremental learning. In: Proceedings of the 5th International workshop on multiple classifier systems, Cagliari, Italy, June 9–11, 2004
41.
Park S-T, Chu W (2009) Pairwise preference regression for cold-start recommendation. In: Proceedings of the 3rd ACM conference on recommender systems, NY, Oct. 23–25, 2009
42.
Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of the ACM KDDCup conference
43.
Polikar R, Udpa L, Udpa S, Honavar V (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transact Syst Man Cybern(C) 31(4):497–508CrossRef
44.
Potonniee O (2004) A decentralized privacy-enabling TV personalization framework. In: Proceedings of the second European conference on interactive television: enhancing the experience (euroITV 2004)
45.
Quinlan R (1986) Induction on decision trees. Mach Learn 1(1):81–106
46.
Quinlan R (1993) C4.5 programs for machine learning. Morgan Kaufmann Publishers, San Francisco
47.
Ramakrishnan N, Keller BJ, Mirza BJ, Grama AY, Karypis G (2001) Privacy issues in recommender systems. IEEE Internet Comput 5(6):54–62CrossRef
48.
Rodriguez JJ, Kuncheva L, Alonzo CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Transact Pattern Anal Mach Intell 28(10):1619–1630CrossRef
49.
Rumelhart DE, Mcleland JL, The PDP research group (1986) Parallel distributed processing explorations in the structure of micro-cognition. vol 1: foundations. MIT Press, Cambridge
50.
Schapire RE, Singer Y (1998) Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the eleventh annual conference on computational learning theory
51.
Smyth B, Cotter PA (2000) A personalized television listing service. Commun ACM 43(8)
52.
Sumner M, Frank E, Hall M (2005) Speeding up logistic model tree induction. In: Proceedings of the ninth European conference on principles and practice of knowledge discovery in databases
53.
Terveen L, Hill W (2001) Human-computer collaboration in recommender systems. In: Carroll J (ed) Human computer interaction in the new millennium. Addison-Wesley, NY
54.
Theodoridis S, Koutroubas K (2006) Pattern recognition, 3rd edn. Academic Press, San DiegoMATH
55.
Tou J, Gonzales RC (1981) Pattern recognition principles, 4th edn. Addison-Wesley, Reading
56.
Vapnik VN (2010) The nature of statistical learning theory. 2nd edn. Springer-Verlag, NY
57.
Wallace M, Akrivas G, Stamou G, Kollias S (2002) Representation of user preferences and adaptation to context in multimedia content-based retrieval. In: Proceedings of the 2002 workshop on multimedia semantics, SOFSEM: theory and practice of informatics
58.
Wang L (2011) An improved multiple fuzzy NNC system based on mutual information and fuzzy integral. Int J Mach Learn Cybern 2(1):25–36CrossRef
59.
Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202MathSciNetMATHCrossRef
60.
Webb GI (2000) Multi-boosting: a technique for combining boosting and bagging. Machine Learning 40(2)
61.
Windeatt T (2005) Diversity measures for multiple classifier systems analysis and design. Inf Fusion 6:21–36CrossRef
62.
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publishers, San FranciscoMATH
63.
Yang B, Tao M, Hua X-S, Yang L, Yang S-Q, Li M (2007) Online video recommendation based on multi-modal fusion and relevance feedback. In: Proceedings of the 6th ACM conference on image and video retrieval, amsterdam, The Netherlands
64.
Zliobaite I (2010) Learning under concept drift: an overview. Cornel University library archives, arXiv:1010.4784v1
Metadata
Title
A classifier ensemble approach to the TV-viewer profile adaptation problem
Authors
Ioannis T. Christou
George Gekas
Anna Kyrikou
Publication date
01-12-2012
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 4/2012
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0066-4

Other articles of this Issue 4/2012

International Journal of Machine Learning and Cybernetics 4/2012 Go to the issue

Original Article

An innovative face image enhancement based on principle component analysis

Original Article

A hierarchical multilevel thresholding method for edge information extraction using fuzzy entropy

Original Article

Learning rates of least-square regularized regression with strongly mixing observation

Original Article

Cascaded cluster ensembles

Original Article

A discernibility matrix for the topological reduction

Original Article

Null space based feature selection method for gene expression data