Context-Aware Personalization Using Neighborhood-Based Context Similarity

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. Contextaware 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 socalled 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.

A Framework for Exploiting Internet of Things for Context-Aware Trust-Based Personalized Services

“…Even though recommendation techniques have been developed to provide users with relevant services from very large corpus of information items, however, their main objective is the explicit service suggestion that is relevant to the user preferences; without considering that, such preferences are dynamic and change according to the user's contexts [11,20,25]. In addition, the recommendation is made when the user explicitly requests for the RS assistance, and the system does not expect that the user's preferences would change with contexts such as time, activities, and locations.…”

“…Since the introduction of contexts into recommendation systems, research in context-aware personalized recommendations has explored contexts extensively for its potentials to improve the relevance of recommendations and thus improve the user's quality of experience and recommendation accuracy [6,11,21]. Nevertheless, CARSs lack the capability to consider context information from various sensing objects in the mobile environments.…”

“…is means that if none of the neighbors has rated the service, the rating prediction cannot be computed. is problem is referred to as cold start problem [11]. Similarly, one key problem of CBF is overspecialization.…”

Towards context classification and reasoning in IoT

“…Context awareness has been extensively explored as a fundamental and key feature of mobile computing systems for adaptive decision making [22]. Therefore, the outcomes of reasoning about information derived from context data should be explored to enhance user's perceived quality of personalized services in ubiquitous environments [21]. However, majority of current IoT systems focus only on raw context data.…”

“…Equipped with sensing and data processing capabilities, IoT infrastructure will incorporate the necessary functionality to sense, pre-process and extract highlevel context knowledge from raw sensor data. This will foster the enrichment as well as understanding of human-to-object or object-to-object interactions, ultimately enhancing the anywhere, anytime delivery of intelligent services, which are tailored to our interests [21].…”