Recommendation algorithm of the app store by using semantic relations between apps

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.

[1]  Jorge García Duque,et al.  Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study , 2008, J. Syst. Softw..

[2]  Eric Horvitz,et al.  Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering , 2000, AAAI/IAAI.

[3]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[4]  Yi-Cheng Ku,et al.  A semantic-expansion approach to personalized knowledge recommendation , 2008, Decis. Support Syst..

[5]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[6]  Edward I. George,et al.  A bayesian model for collaborative filtering , 1999, AISTATS.

[7]  Konstantinos G. Margaritis,et al.  Unison-CF: A Multiple-Component, Adaptive Collaborative Filtering System , 2004, AH.

[8]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[9]  Qiang Yang,et al.  Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.

[10]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[11]  Steffen Staab,et al.  Learning Ontologies for the Semantic Web , 2001 .

[12]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[13]  Sanggil Kang,et al.  A Novel Personalized Paper Search System , 2006, ICIC.

[14]  Mirta Baranovic,et al.  Semantically enhanced web personalization approaches and techniques , 2010, Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces.

[15]  Korris Fu-Lai Chung,et al.  Knowledge and Information Systems , 2017 .

[16]  Paolo Avesani,et al.  Trust-Aware Collaborative Filtering for Recommender Systems , 2004, CoopIS/DOA/ODBASE.

[17]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[18]  Jorge García Duque,et al.  Receiver-side semantic reasoning for digital TV personalization in the absence of return channels , 2009, Multimedia Tools and Applications.