Context-aware media recommendations for smart devices

Abstract

The emergence of pervasive computing, the rapid advancements in broadband and mobile networks and the incredible appeals of smart devices are driving unprecedented universal access and delivery of online-based media resources. As more and more media services continue to flood the Web, mobile users will continue to waste invaluable time, seeking content of their interest. To deliver relevant media items offering richer experiences to mobile users, media services must be equipped with contextual knowledge of the consumption environment as well as contextual preferences of the users. This article investigates context-aware recommendation techniques for implicit delivery of contextually relevant online media items. The proposed recommendation services work with a contextual user profile and a context recognition framework, using case based reasoning as a methodology to determine user's current contextual preferences, relying on a context recognition framework to identify user's dynamic contextual situations from device's built-in sensors. To evaluate the proposed solution, we developed a case-study context-aware application that provides personalized recommendations adapted to user's current context, namely the activity he/she performs and consumption environment constraints. Experimental evaluations, via the case study application, real-world user data, and online-based movie metadata, demonstrate that context-aware recommendation techniques can provide better efficacy than the traditional approaches. Additionally, evaluations of the underlying context recognition process show that its power consumption is within an acceptable range. The recommendations provided by the case study application were assessed as effective via a user study, which demonstrates that users are pleased with the contextual media recommendations.

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

“…Considering the overwhelming volume of information generated by billions of devices today, the information overload problem cannot be overemphasized [19]. To address this socalled "information overload problem," in which information consumers spend invaluable time to nd relevant services due to the humongous volume of myriad available alternatives, personalized recommendation techniques have been extensively explored to deliver such relevant services to users according to their personal interests [6,13,[20][21][22][23]. e traditional recommendation systems take information about items and users and process this information to suggest items of interest to target users.…”

“…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.…”

“…Generally, in the design and development of frameworks for CARSs, diverse contextual information has been used, for example, location, time, weather information, environment conditions such as temperature, illumination, and noise level, device characteristics, and network conditions. is information is usually collected from the mobile devices, especially smartphones [20]. However, relying only on context information obtained from mobile phones to provide service recommendations in the ubiquitous environment limits the ability of the system to truly learn the preferences of users as their contextual situations and environment conditions change.…”

Context-Aware Personalization Using Neighborhood-Based Context Similarity

“…This information is obtained either explicitly or implicitly as described in our previous work [10,20,28]. In [10], we analyzed how contextual user profile model can be used in context-aware content-based recommendations.…”

“…To address this kind of cold-start problem, different techniques based on context-aware personalized recommendations have been proposed, assisting mobile users to obtain content according to their contextual preferences [1,2,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Figure 1 illustrates a typical architecture of a context-aware personalized recommendation system, exemplifying the system being proposed in this article.…”