Abstract
In this paper, we propose a personalized recommendation system for mobile application software (app) to mobile user using semantic relations of apps consumed by users. To do that, we define semantic relations between apps consumed by a specific member and his/her social members using Ontology. Based on the relations, we identify the most similar social members from the reasoning process. The reasoning is explored from measuring the common attributes between apps consumed by the target member and his/her social members. The more attributes shared by them, the more similar is their preference for consuming apps. We also develop a prototype of our system using OWL (Ontology Web Language) by defining ontology-based semantic relations among 50 mobile apps. Using the prototype, we showed the feasibility of our algorithm that our recommendation algorithm can be practical in the real field and useful to analyze the preference of mobile user.
Similar content being viewed by others
References
ReadWriteWeb (2011) http://www.readwriteweb.com/archives/mobile_app_marketplace_175_billion_by_2012.php. Accessed 2 March 2011
Liang T-P, Yang Y-F, Chen D-N, Ku Y-C (2008) A semantic expansion approach to personalized knowledge recommendation. Decis Support Syst 45:401–412. doi:10.1016/j.dss.2007.05.004
Kim J, Heo N, Kang S (2010) Digital TV content recommendation method based on individual ontology and stereotype user group ontology. Inf Int Interdiscip J 13(5):1679–1691
AppStore HQ (2011) http://www.appstorehq.com. Accessed 3 March 2011
Appolicious (2011) http://www.appolicious.com. Accessed 3 March 2011
Smokin Apps (2011) http://smokinapps.com. Accessed 3 March 2011
Genius Recommendations (2011) http://www.appleinsider.com/articles/10/08/06/apple_adds_genius_recommendation_tab_to_ipad_app_store.html. Accessed 4 March 2011
Maedche A, Staab S (2001) Learning ontologies for the semantic web. In: Semantic Web Workshop, Hong Kong, China
Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, López-Nores M, García-Duque J, Fernández-Vilas A, Díaz-Redondo RP (2008) Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: a case study. J Syst Softw 81:2371–2385. doi:10.1016/j.jss.2008.05.009
Vuljanic D, Rovan L, Baranovic M (2010) Semantically enhanced web personalization approaches and techniques. In: Proceedings of the ITI 2010 32nd int conf on information technology interfaces, Cavtat, Croatia, pp 217–222
Kang S, Cho Y (2006) A novel personalized paper search system. In: Lecture notes in computer science, vol 4113. Springer, Berlin, pp 1257–1262. doi:10.1007/11816157_157
Balabanovic M, Shoham Y (1997) FAB: Content-based, collaborative recommendation. Commun ACM 40(3):66–72
Melville P, Mooney RJ, Nagarajan R (2002) Content-boosted collaborative filtering for improved recommendations. In: Proceeding of eighteenth national conference on artificial intelligence, Edmonton, Alberta, Canada, pp 187–192
Xue GR, Lin C, Yang Q, Xi WS, Zeng HJ, Yu Y, Chen Z (2005) Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 2005 ACM SIGIR conference, Salvador, Brazil, pp 114–121
Pennock DM, Horvitz E, Lee Giles C (2000) Social choice theory and recommender systems: analysis of the axiomatic foundations of collaborative filtering. In: Proceedings of the seventeenth national conference on artificial intelligence (AAAI-2000), pp 729–734
Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: Lecture notes in computer science, vol 3290, pp 492–508. Springer, Berlin. doi:10.1007/978-3-540-30468-5_31
Chien YH, George EI (1999) A Bayesian model for collaborative filtering. In: Proceedings of the seventh international workshop on artificial intelligence and statistics. Morgan Kaufmann, San Francisco
Vozalis M, Margaritis K (2004) Unison-CF: a multiple-component, adaptive collaborative filtering system. In: Proceedings of the third international conference on adaptive hypermedia and adaptive web-based systems (AH 2004). Lecture notes in computer science, vol 3137. Springer, Berlin, pp 255–264
Leung CW, Chan SC, Chung F (2006) A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3):357–381. doi:10.1007/s10115-006-0002-1
Burke R (2007) The adaptive web—methods and strategies of web personalization. In: Hybrid web recommender systems. Springer, Berlin, pp 377–408
Resnick P, Iacovou N, Suchak M, Bergstorm P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: ACM conference on computer supported cooperative work.
López-Nores M, Blanco-Fernández Y, Pazos-Arias J, García-Duque J, Ramos-Cabrer M, Gil-Solla A, Díaz-Redondo R, Fernández-Vilas A (2009) Receiver-side semantic reasoning for digital TV personalization in the absence of return channels. Multimed Tools Appl 41(3):407–436. doi:10.1007/s11042-008-0239-7
Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on digital libraries, pp 195–204
Cohen J (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, J., Kang, S., Lim, Y. et al. Recommendation algorithm of the app store by using semantic relations between apps. J Supercomput 65, 16–26 (2013). https://doi.org/10.1007/s11227-011-0701-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-011-0701-6