nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering

verfasst von : Aleksandra Karpus, Iacopo Vagliano, Krzysztof Goczyła

Erschienen in: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Context-aware Recommender Systems aim to provide users with better recommendations for their current situation. Although evaluations of recommender systems often focus on accuracy, it is not the only important aspect. Often recommendations are overspecialized, i.e. all of the same kind. To deal with this problem, other properties can be considered, such as serendipity. In this paper, we study how an ontology-based and context-aware pre-filtering technique which can be combined with existing recommendation algorithm performs in ranking tasks. We also investigate the impact of our method on the serendipity of the recommendations. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can improve the accuracy and serendipity.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Sorting Data on Ultra-Large Scale with RADULS

Nächstes Kapitel Extending Expressiveness of Knowledge Description with Contextual Approach

https://en.oxforddictionaries.com/definition/serendipity.

http://softeng.polito.it/rsctx/.

http://ns.inria.fr/prissma.

http://www.w3.org/2005/Incubator/model-based-ui/XGR-mbui/.

http://vocab.deri.ie/rooms.

rdfs.co/juso/latest/html.

https://www.w3.org/2006/time.

http://www.librec.net/.

https://github.com/padamop/ConcertTweets.

Adamopoulos, P., Tuzhilin, A.: Estimating the value of multi-dimensional data sets in context-based recommender systems. In: 8th ACM Conference on Recommender Systems (RecSys 2014) (2014)

Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to better expect the unexpected. ACM Trans. Intell. Syst. Technol. 5(4), 54:1–54:32 (2014). http://doi.acm.org/10.1145/2559952 CrossRef

Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., et al. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, US (2011). doi:10.1007/978-0-387-85820-3_7 CrossRef

Anand, S.S., Kearney, P., Shapcott, M.: Generating semantically enriched user profiles for web personalization. ACM Trans. Internet Technol. 7(4), 22:1–22:26 (2007). http://doi.acm.org/10.1145/1278366.1278371

Cantador, I., Bellogín, A., Castells, P.: Ontology-based personalised and context-aware recommendations of news items. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, vol. 1, pp. 562–565. IEEE Computer Society, Washington (2008). http://dx.doi.org/10.1109/WIIAT.2008.204

Costabello, L.: A declarative model for mobile context. In: Context-Aware Access Control and Presentation for Linked Data, pp. 21–32 (2013)

Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001). http://dx.doi.org/10.1007/s007790170019 CrossRef

Di Noia, T., Ostuni, V.C.: Recommender systems and linked open data. In: Faber, W., Paschke, A. (eds.) Reasoning Web 2015. LNCS, vol. 9203, pp. 88–113. Springer, Cham (2015). doi:10.1007/978-3-319-21768-0_4 CrossRef

Goczyła, K., Waloszek, A., Waloszek, W.: Hierarchiczny podział przestrzeni ontologii na kontekst. In: Bazy Danych: Nowe Technologie, pp. 247–260. WKŁ, Gliwice (2007)

10.

Goczyła, K., Waloszek, A., Waloszek, W.: Techniki modularyzacji ontologi. In: Bazy Danych: Rozwój metod i technologii, pp. 311–324. WKŁ, Gliwice (2008)

11.

Goczyla, K., Waloszek, A., Waloszek, W.: Towards context-semantic knowledge bases. In: Proceedings of the Federated Conference on Computer Science and Information Systems - FedCSIS 2012, Wroclaw, Poland, pp. 475–482, 9–12 September 2012. https://fedcsis.org/proceedings/2012/pliks/388.pdf

12.

Goczyła, K., Waloszek, A., Waloszek, W., Zawadzka, T.: Modularized knowledge bases using contexts, conglomerates and a query language. In: Bembenik, R., et al. (eds.) Intelligent Tools for Building a Scientific Information Platform. SCI, vol. 390, pp. 179–201. Springer, Heidelberg (2012). doi:10.1007/978-3-642-24809-2_11 CrossRef

13.

Goczyla, K., Waloszek, W., Waloszek, A.: Contextualization of a DL knowledge base. In: Proceedings of the 2007 International Workshop on Description Logics (DL 2007) (2007). http://ceur-ws.org/Vol-250/paper_55.pdf

14.

Griffiths, T.: Gibbs sampling in the generative model of Latent Dirichlet Allocation. Technical report, Stanford University (2002). www-psych.stanford.edu/~gruffyddcogsci02/lda.ps

15.

Hawalah, A., Fasli, M.: Utilizing contextual ontological user profiles for personalized recommendations. Expert Syst. Appl. 41(10), 4777–4797 (2014). http://www.sciencedirect.com/science/article/pii/S0957417414000633 CrossRef

16.

Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRef

17.

Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE Computer Society, Washington (2008). http://dx.doi.org/10.1109/ICDM.2008.22

18.

Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Molino, P.: Can a recommender system induce serendipitous encounters? In: E-Commerce, pp. 1–17. IN-TECH, Vienna (2009)

19.

Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 659–667. ACM, New York (2013). http://doi.acm.org/10.1145/2487575.2487589

20.

Karpus, A., Vagliano, I., Goczyła, K., Morisio, M.: An ontology-based contextual pre-filtering technique for recommender systems. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 411–420, September 2016

21.

Kotkov, D., Veijalainen, J., Wang, S.: Challenges of serendipity in recommender systems. In: Proceedings of the 12th International Conference on Web Information Systems and Technologies, WEBIST, vol. 2, pp. 251–256 (2016)

22.

Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., et al. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, US (2011). doi:10.1007/978-0-387-85820-3_3 CrossRef

23.

Maksai, A., Garcin, F., Faltings, B.: Predicting online performance of news recommender systems through richer evaluation metrics. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, pp. 179–186. ACM, New York (2015). http://doi.acm.org/10.1145/2792838.2800184

24.

Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004). http://doi.acm.org/10.1145/963770.963773 CrossRef

25.

Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. In: Berendt, B., Hotho, A., Mladenič, D., Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 57–76. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30123-3_4 CrossRef

26.

Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 502–511. IEEE Computer Society, Washington (2008). http://dx.doi.org/10.1109/ICDM.2008.16

27.

Rack, C., Arbanowski, S., Steglich, S.: Context-aware, ontology-based recommendations. In: Proceedings of the International Symposium on Applications on Internet Workshops, SAINT-W 2006, pp. 98–104. IEEE Computer Society, Washington (2006). http://dx.doi.org/10.1109/saint-w.2006.13

28.

Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461. AUAI Press, Arlington (2009). http://dl.acm.org/citation.cfm?id=1795114.1795167

29.

Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, US (2011). doi:10.1007/978-0-387-85820-3_1 CrossRef

30.

Rodríguez, J., Bravo, M., Guzmán, R.: Multidimensional ontology model to support context-aware systems (2013). http://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/view/7187

31.

Su, Z., Yan, J., Ling, H., Chen, H.: Research on personalized recommendation algorithm based on ontological user interest model. J. Comput. Inf. Syst. 8(1), 169–181 (2012)

32.

Weijnen, M., Drinkenburg, A. (eds.): Precision Process Technology: Perspectives for Pollution Prevention. Springer Science & Business Media, Dordrecht (2012)

33.

Zhang, Y.C., Séaghdha, D.O., Quercia, D., Jambor, T.: Auralist: introducing serendipity into music recommendation. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 13–22. ACM, New York (2012). http://doi.acm.org/10.1145/2124295.2124300

34.

Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 22–32. ACM, New York (2005). http://doi.acm.org/10.1145/1060745.1060754

Titel: Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering
verfasst von: Aleksandra Karpus
Iacopo Vagliano
Krzysztof Goczyła
Verlag: Springer International Publishing
Buch: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation
Print ISBN: 978-3-319-58273-3

Electronic ISBN: 978-3-319-58274-0

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-58274-0_21

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner