Abstract
Context-aware recommender systems (CARSs) have emerged from traditional recommender systems (RSs) that provide several different opportunities in the area of personalized recommendations for online users. CARSs promote incorporation of additional contextual information such as time, day, season, user’s personality along with users and items related information into recommendation process that makes market based e-commerce sites more attractive to users. Content-based filtering (CBF) and collaborative filtering (CF) are two well-known and most implemented recommendation techniques that offer various hybridization approaches for producing quality recommendations. Moreover, contextual pre-filtering, contextual post-filtering and contextual modeling are some paradigms through which CARSs take advantages of user’s contextual preferences in recommendation process. In this paper, we introduce a decision tree based CARS framework that exploits the benefits of both CBF and CF techniques using contextual pre-filtering paradigm. We apply ID3 algorithm for learning a user model to exploit the user’s contextual preferences and utilizing rules extracted from decision tree to neighborhood formation. Experimental results using two real-world benchmark datasets clearly validate the effectiveness of our proposed scheme in comparison to traditional scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Stewart, A., Niederée, C., Mehta, B.: State of the Art in user modeling for personalization in content, service and interaction. NSF/DELOS Report on Personalization, pp. 1–6 (2004)
Adomavicius, G., Tuzhilin, A.: 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)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_7
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
Kim, K., Ahn, H., Jeong, S.: Context-aware recommender systems using data mining techniques. World Acad. Sci., Eng. Technol. Int. J. Ind. Manuf. Eng. 4(4), 381–386 (2010)
Imran, H., Belghis-Zadeh, M., Chang, T.W., Kinshuk, G.S.: A rule-based recommender system to suggest learning tasks. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) Intelligent Tutoring Systems. ITS 2014. LNCS, vol. 8474, pp. 672–673. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07221-0_102
Daniele, L., Dockhorn Costa, P., Ferreira Pires, L.: Towards a rule-based approach for context-aware applications. In: Pras, A., van Sinderen, M. (eds.) EUNICE 2007. LNCS, vol. 4606, pp. 33–43. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73530-4_5
Rack, C., Arbanowski, S., Steglich, S.: A generic multipurpose recommender system for contextual recommendations. In: 8th International Symposium Proceedings on Autonomous Decentralized Systems (ISADS’07), pp. 445–450. IEEE, USA (2007)
Gershman, A., Meisels, A., Lüke, K.H., Rokach, L., Schclar, A., Sturm, A.: A decision tree based recommender system. In: 10th International Conference Proceedings on Innovative Internet Community Services, Bangkok, pp. 170–179 (2010)
Agarwal, V., Bharadwaj, K.K.: A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Soc. Netw. Anal. Min. 3(3), 359–379 (2012)
Kuflik, T., Kay, J., Kummerfeld, B.: Challenges and solutions of ubiquitous user modeling. In: Krüger, A., Kuflik, T. (eds.) Ubiquitous Display Environments. Cognitive Technologies, pp. 7–30. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27663-7_2
Patidar, A., Agarwal, V., Bharadwaj, K.K.: Predicting friends and foes in signed networks using inductive inference and social balance theory. In: IEEE/ACM International Conference Proceedings on Advances in Social Networks Analysis and Mining, pp. 384–388. IEEE, Turkey (2012)
Bobek, S., Nalepa, G.J.: Uncertainty handling in rule-based mobile context-aware systems. Pervasive Mob. Comput. 39, 159–179 (2017)
Bobek, S., Misiak, P.: Uncertain decision tree classifier for mobile context-aware computing. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, Jacek M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10842, pp. 276–287. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91262-2_25
Jenhani, I., Amor, N.B., Elouedi, Z.: Decision trees as possibilistic classifiers. Int. J. Approximate Reasoning 48, 784–807 (2008)
Han, J., Kamber, M., Pei, J.: Data Mining. Concepts and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)
Tolun, M.R., Sever, H., Uludag, M., Abu-Soud, S.M.: ILA-2: an inductive learning algorithm for knowledge discovery. Cybern. Syst. 30(7), 609–628 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Linda, S., Bharadwaj, K.K. (2018). A Decision Tree Based Context-Aware Recommender System. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-04021-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04020-8
Online ISBN: 978-3-030-04021-5
eBook Packages: Computer ScienceComputer Science (R0)