Discerning Relevant Model Features in a Content-based Collaborative Recommender System

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche

Reference

Alejandro Bellogín, Iván Cantador, Pablo Castells, Álvaro Ortigosa: Discerning Relevant Model Features in a Content-based Collaborative Recommender System. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 429-455.

DOI

http://dx.doi.org/10.1007/978-3-642-14125-6_20

Abstract

Recommender systems suggest users information items they may be interested in. User profiles or usage data are compared with some reference characteristics, which may belong to the items (content-based approach), or to other users in the same context (collaborative filtering approach). These items are usually presented as a ranking, where the more relevant an item is predicted to be for a user, the higher it appears in the ranking. In this scenario, a preferential order has to be inferred, and therefore, preference learning methods can be naturally helpful. The relevant recommendation model features for the learning-based enhancements explored in this work comprise parameters of the recommendation algorithms, and user-related attributes. In the researched approach, machine learning techniques are used to discover which model features are relevant in providing accurate recommendations. The assessment of relevant model features, which is the focus of this paper, is envisioned as the first step in a learning cycle in which improved recommendation models are produced and executed after the discovery step, based on the findings that result from it.

Extended Abstract

Bibtex

@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_20},
title={Discerning Relevant Model Features in a Content-based Collaborative Recommender System},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_20, http://de.evo-art.org/index.php?title=Discerning_Relevant_Model_Features_in_a_Content-based_Collaborative_Recommender_System },
publisher={Springer Berlin Heidelberg},
author={Bellogín, Alejandro and Cantador, Iván and Castells, Pablo and Ortigosa, Álvaro},
pages={429-455},
language={English}
}

Used References

1. G. Adomavicius, R. Sankaranarayanan, S. Sen, A. Tuzhilin, Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005) http://dx.doi.org/10.1145/1055709.1055714

2. G. Adomavicius, A. Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005) http://dx.doi.org/10.1109/TKDE.2005.99

3. K. Becker, C.G. Marquardt, D.D. Ruiz, A Pre-Processing Tool for Web Usage Mining in the Distance Education Domain, in Proceedings of the 8th International Database Engineering and Applications Symposium (IDEAS 2004) (2004), pp. 78–87

4. A. Bellogín, I. Cantador, P. Castells, A. Ortigosa, Discovering Relevant Preferences in a Personalised Recommender System using Machine Learning Techniques, in Proceedings of the ECML/PKDD-08 Workshop on Preference Learning (PL 2008), pp. 82–96

5. P. Brusilovsky, Developing adaptive educational hypermedia systems: From design models to authoring tools, in Authoring Tools for Advanced Technology Learning Environment (2003), pp. 377–409

6. R. Burke, Hybrid Recommender Systems: Survey and Experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002) http://dx.doi.org/10.1023/A%3A1021240730564

7. I. Cantador, A. Bellogín, P. Castells, A Multilayer Ontology-based Hybrid Recommendation Model. AI Commun. 21(2-3), 203–210 (2008)MathSciNetMATH

8. I. Cantador, A. Bellogín, P. Castells, News@hand: A Semantic Web Approach to Recommending News, in Proceedings of the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2008) (2008), pp. 279–283

9. I. Cantador, A. Bellogín, P. Castells, Ontology-based Personalised and Context-aware Recommendations of News Items, in Proceedings of the 2008 Web Intelligence Conference (2008), pp. 562–565

10. I. Cantador, P. Castells, Extracting Multilayered Semantic Communities of Interest from Ontology-based User Profiles: Application to Group Modelling and Hybrid Recommendations. Computers in Human Behavior (Elsevier, 2008)

11. I. Cantador, M. Szomszor, H. Alani, M. Fernández, P. Castells, Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations, in Proceedings of the 1st Intl. Workshop on Collective Intelligence and the Semantic Web (CISWeb 2008) (2008),pp. 5–19

12. P. Castells, M. Fernández, D. Vallet, An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval. IEEE Trans. Knowl. Data Eng. 19(2), 261–272 (2007) http://dx.doi.org/10.1109/TKDE.2007.22

13. P.R. Cohen, R. Kjeldsen, Information Retrieval by Constrained Spreading Activation in Semantic Networks. Inf. Process. Manag. 23(4), 255–268 (1987) http://dx.doi.org/10.1016/0306-4573(87)90017-3

14. F. Crestani, Application of Spreading Activation Techniques in Information Retrieval. Artif. Intell. Rev. 11(6), 453–482 (1997) http://dx.doi.org/10.1023/A%3A1006569829653

15. F. Crestani, P.L. Lee, Searching the Web by Constrained Spreading Activation. Inf. Process. Manag. 36(4), 585–605 (2000) http://dx.doi.org/10.1016/S0306-4573(99)00073-4

16. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification (Wiley-InterScience, 2000)

17. R. Rafter, B. Smyth, Conversational Collaborative Recommendation: An Experimental Analysis. Artif. Intell. Rev. 24(3-4), 301–318 (2005) http://dx.doi.org/10.1007/s10462-005-9004-8

18. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, Grouplens: An open architecture for collaborative filtering on netnews, in Proceedings of the 1994 Conference on. Computer Supported Collaborative Work (1994), pp. 175–186

19. B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in Proceedings of the 2001 WWW Conference (2001), pp. 285–295

20. J. Srivastava, R. Cooley, M. Deshpande, P. Tan, Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explor. 1(2), 12–23 (2000) http://dx.doi.org/10.1145/846183.846188

21. L. Talavera, E. Gaudioso, Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces, in Proceedings Workshop on AI in CSCL (2004), pp. 17–23

22. L. Terveen, W. Hill, Beyond Recommender Systems: Helping People Help Each Other, in Human-Computer Interaction in the New Millennium (2001), pp. 487–509

23. D. Vallet, P. Castells, M. Fernández, P. Mylonas, Y. Avrithis, Personalised Content Retrieval in Context Using Ontological Knowledge. IEEE TCSVT 17(3), 336–346 (2007)

24. C. Vialardi, J. Bravo, A. Ortigosa, Empowering AEH Authors Using Data Mining Techniques, in Proceedings of the 5th Int. Workshop on Authoring of Adaptive and Adaptable Hypermedia (2007)

25. C. Vialardi, J. Bravo, A. Ortigosa, Improving AEH Courses through Log Analysis. J. Univers. Comput. Sci. 14(17), 2777–2798 (2008)

26. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems, 2005)

27. O.R. Zaïane, Recommender System for E-learning: Towards Non-Instructive Web Mining, in Data Mining in E-Learning, ed. by C. Romero, S. Ventura (2006), pp. 79–96

28. T. Zhang, V.S. Iyengar, Recommender Systems Using Linear Classifiers J. Mach. Learn. Res. 2, 313–334 (2002)MATH


Links

Full Text

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.4032&rep=rep1&type=pdf

intern file

Sonstige Links

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.4032