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Context-Aware Personalization Using Neighborhood-Based Context Similarity

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Abstract

With the overwhelming volume of online multimedia content and increasing ubiquity of Internet-enabled mobile devices, pervasive use of the Web for content sharing and consumption has become our everyday routines. Consequently, people seeking online access to content of interest are becoming more and more frustrated. Thus, deciding which content to consume among the deluge of available alternatives becomes increasingly difficult. Context-aware personalization, having the capability to predict user’s contextual preferences, has been proposed as an effective solution. However, some existing personalized systems, especially those based on collaborative filtering, rely on rating information explicitly obtained from users in consumption contexts. Therefore, these systems suffer from the so-called cold-start problem that occurs as a result of personalization systems’ lack of adequate knowledge of either a new user’s preferences or of a new item rating information. This happens because these new items and users have not received or provided adequate rating information respectively. In this paper, we present an analysis and design of a context-aware personalized system capable of minimizing new user cold-start problem in a mobile multimedia consumption scenario. The article emphasizes the importance of similarity between contexts of consumption based on the traditional k-nearest neighbor algorithm using Pearson Correlation model. Experimental validation, with respect to quality of personalized recommendations and user satisfaction in both contextual and non-contextual scenarios, shows that the proposed system can mitigate the effect of user-based cold-start problem.

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Acknowledgments

This work was partly financed by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) with grant SFRH/BD/69517/2010. The authors would also like to thank all the online participants who voluntarily participated in the user profile data collection and evaluation of the system.

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  1. Abayomi Moradeyo Otebolaku

    Present address: Department of Electronics, Telecommunications and Informatics, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

Authors and Affiliations

  1. Center for Telecommunications and Multimedia, INESC TEC, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465, Porto, Portugal

    Abayomi Moradeyo Otebolaku & Maria Teresa Andrade

  2. Faculty of Engineering, University of Porto, Porto, Portugal

    Maria Teresa Andrade

Authors
  1. Abayomi Moradeyo Otebolaku
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  2. Maria Teresa Andrade
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Correspondence to Abayomi Moradeyo Otebolaku.

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Otebolaku, A.M., Andrade, M.T. Context-Aware Personalization Using Neighborhood-Based Context Similarity. Wireless Pers Commun 94, 1595–1618 (2017). https://doi.org/10.1007/s11277-016-3701-2

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