Context-Aware Personalization Using Neighborhood-Based Context Similarity

• Type: Article in International Scientific Journal
• Date: 2016-09-15

Authors

Maria Teresa Andrade (Author) FEUP; Abayomi Moradeyo Otebolaku (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Springer Wireless Personal Communications

Vol. 94 No. 3

Pages: 1595-1618

ISSN: 0929-6212

Indexing

ISI Web of Knowledge - 6 Citations

ISI Web of Science

Scopus - 10 Citations

INSPEC

Other information

Authenticus ID: P-00K-XKC

DOI: 10.1007/s11277-016-3701-2

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24