Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation

2016

Wireless Pers Commun

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.

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Section: Discussionmentioning

confidence: 99%

“…This is possible because we have developed a dynamic context recognition model as an integral part of our broader personalization system [24,28,31]. First, the model acquires low-level context data from the user's handheld device to classify and identify their contextual situations.…”

Section: A High-level View Of New User's Preference Prediction Using mentioning

confidence: 99%

Context-Aware Personalization Using Neighborhood-Based Context Similarity

2016

Wireless Pers Commun

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“…Though the work presented in this article is similar to the above-referred solutions, however, rather than relying on user ratings, which are not always available in practice, it relies on a contextual user profile model that tracks in real-time the user's contextual preferences [12]. It also relies on a context pre-filtering paradigm rather than on the context-modeling paradigm [8].…”

Section: Related Workmentioning

confidence: 99%

“…The first component is the context recognition process model, which runs on the user's phone, monitoring, learning, and predicting the user's contexts. It gathers events from the user's mobile device builtin sensors, preprocessing them, and inferring user's high-level context as described in [12]. The high-level context is then instantiated into a context ontology to realize a much more meaningful high-level contextual information as described in [13].…”

Section: I Proposed Context-aware Content Based Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

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

2014 28th International Conference on Advanced Information Networking and Applications Workshops

2014

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.

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A Context-Aware Framework for Media Recommendation on Smartphones

Lecture Notes in Electrical Engineering

2014