Context-aware recommendations through context and activity recognition in a mobile environment

Abstract

The mobile Internet introduces new opportunities to gain insight in the user's environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user's current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.

A user-centric evaluation of context-aware recommendations for a mobile news service

“…For contextaware music recommendations for example, the user's emotions can be used as input by using support vector machines as emotional state transition classifier [22]. In the application domain of tourism, various applications use the current location or activity of the user to personalize and adapt their content offer to the current user needs [31,15]. Personal recommendations for points of interest can be provided based on the user's proximity of the venue [26].…”

Context-Aware Personalization Using Neighborhood-Based Context Similarity

“…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.…”

“…Another interesting work by Pessemier et al [11] uses daily activity contexts and other contextual information to provide personalized content according to the current activity a user performs, using collaborative recommendations. Also, a recent proposal by Alhamid et al [14] uses social tags and user rating information to personalize multimedia content search in a context-aware collaborative recommendation system.…”

“…the similarity of these context dimensions is computed and summed as illustrated in Eq. (11). t in (11) is a normalizing factor such that the absolute values of the context similarity sum to unity.…”

Enhancing Recommender Systems for TV by Face Recognition

“…With the growing popularity of mobile devices and their increased connectivity, recommender systems have expanded their area of application to the mobile platform. Additional information about the user, which is accessible through the camera, microphone, or sensors such as gyroscope and GPS, allow to further improve the accuracy of the recommendations and adjust them to the current user context (De Pessemier et al, 2014b). The accessibility and popularity of operating systems such as Android further stimulate the development of recommendation tools.…”