Context-Aware User Profiling and Multimedia Content Classification for Smart Devices

Abstract

Current solutions for delivering adapted multimedia content to mobile users take into account only a limited set of contextual information, and can be seen as passive solutions. We propose a solution that anticipates user's needs based on the contexts of use and preferences, to deliver media content to users in mobile environments. This article describes the profiling approach of the proposed solution, and a context-aware contentbased recommendation for smart devices. Recommendations are issued based on user history, driven by real-time contextual conditions.

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.…”

“…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. But here, we present an overview of the contextual user profile model and explain in the next section how we have extended it to utilize the similarity of users' contexts to predict preferences in a neighborhood(collaborative) process.…”

“…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.…”

“…This problem is usually addressed by asking these new users to provide their preferences explicitly before they can be provided with recommendations [9]. Additionally, unlike collaborative-based recommendation systems, content-based recommendation systems suffer from overspecialization, which occurs when they provide users with only those items that are similar to those they have consumed in the past [2,3,5,10,23]. Furthermore, if a user has no record of consumption, it becomes difficult to provide such user with recommendations.…”

“…In our previous work, we described how contextual information can be incorporated into content-based traditional recommendation systems via a novel user profiling approach to provide multimedia content recommendations [10]. However, in that project, similar to the content-based approach proposed by Wang et al [22], we only used information about content consumed in the past by users to provide contextual recommendations in their current contextual situations.…”