Context-Aware Personalization for Mobile Services

Context-Aware Personalization for Mobile Services

Abayomi Moradeyo Otebolaku (Liverpool John Moores University, UK) and Maria Teresa Andrade (University of Porto, Portugal)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/978-1-5225-2255-3.ch524
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Abstract

Audiovisual content consumption on mobile platforms is rising exponentially and this trend will continue in the next years as mobile devices become more sophisticated. Thus, smartphones are gradually replacing our desktops as they increasingly become cheaper and more powerful with excellent multimedia processing support. As mobile users go about their routines, they continuously browse the Web, seeking interesting content to consume, and also uploading personal content. However, users encounter huge volume of content, that does not match their preferences, resulting in mobile information overload. Context-aware media personalization (CAMP) was proposed as a solution to this problem. CAMP assists users to select relevant content among alternatives considering users' preferences and contexts. This solution, however, are limited to static contexts. Our contribution is Mobile Context-Aware Media Personalization(MobCAMP), which is a special kind of personalization that utilizes user's contexts and activities to suggest media content according to the user's tastes and contextual situations.
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Introduction

The last decade has witnessed an unprecedented proliferation of multimedia-enabled mobile devices and an escalation of online multimedia content. In fact, consumption of audiovisual content in diverse mobile platforms has risen exponentially and this trend is expected to continue in the next few years as mobile devices become more and more sophisticated. According to Gartner, a world leading information technology research and advisory company, Smartphone sales represented two-thirds of global mobile phone market in 2014 with over 1.2 billion units sold (Gartner, 2015). Thus, it appears, smartphones are gradually replacing our desktops as they increasingly become cheaper and more powerful (in terms of processing capability, network connectivity) with excellent multimedia processing support (Flora, 2010; Ricci, 2011). This development indicates global penetration and increasing acceptance of smartphones as the primary platform for information access and processing.

As mobile users go about their daily routines, they continuously browse the Web, seeking interesting content to consume, and occasionally also uploading their personal content. However, users encounter huge volume of content, which often does not match their preferences resulting in what we call mobile information overload. To address this problem, user preferences are usually learned to predict relevant content. However, user preferences are dynamic, changing as users move from one place to another, performing different activities. Therefore, it is important to mine, learn and understand contexts in which users perform such activities. This contextual information can be used to filter, customize and deliver interesting content, the process generally referred to as context-aware personalization. Its main goal is to assist users to overcome mobile information overload by selecting from overwhelming set of potential choices, services, etc. that match user’s context dependent content to target users, tailoring such services to user’s tastes, network and device characteristics. Consequently, users can focus more on important activities, minimizing distractions and time, while consuming multimedia services.

Context-aware media personalization (CAMP) has been the focus of researchers addressing mobile multimedia information overload problem in the last years (Ricci, 2011; Otebolaku &Andrade, 2015a; Lee et. al,2015). CAMP assists users to select content among a deluge of alternatives by considering users’ preferences and contexts to improve their consumption experience. Existing solutions of this kind, however, are limited to static preferences. The traditional personalization solutions only consider user preferences without contexts such as location, activities, etc. Static solutions are not effective in mobile environments as users are increasingly mobile, moving from one location to another, engaging in diverse activities. For example, the type of content a user would consume at home would be, in most cases, different from those they would consume in the office or at airports. Even those solutions that consider contexts have relied on explicit contextual information. For example, they usually ask users to provide their current contexts. Systems relying on this kind of static context information have not been able to address information overload problem. Thus, our contribution to CAMP is Mobile Context-Aware Media Personalization(MobCAMP), which we define as a special type of personalization that utilizes user’s contexts and activities to select and adapt media content according to user’s tastes and contextual situations. Recommendation algorithms currently are the most popular techniques for realizing personalization, whilst context-aware recommendations deal with providing suggestions to users when and where content are needed, context-aware personalization can be used to provide such suggestions when, where and how such content is required.

Key Terms in this Chapter

Smartphone: Is a mobile phone with sophisticated processing and computing capabilities, running an advanced operating system such as Android, Windows mobile, IOS, etc.

Contextual User Profile: In context-aware personalized recommendations, a contextual user profile contains user’s multimedia consumption preferences/interests in relation to contextual information such as location, optional personal information, user’s device characteristics, and network information.

Smartphone Built-In Sensors: These are sensors including accelerometers, gyroscopes, GPSs, Wi-Fi, proximity sensors, etc. embedded in commercial smartphones.

Context Recognition: Is a process that identifies user’s real-time contextual situations from sensory data, using pattern recognition, signal processing and machine learning algorithms.

Target User: Is a user whose profile is currently being processed by the recommendation system. It can also be considered as a user to whom recommendations are being provided.

Context-Aware Personalized Collaborative Recommendation: Is a personalized recommendation technique that suggests items, using items that other users have consumed in contexts similar to the target user’s context.

Context-Aware Personalized Content-Based Recommendation: Is a personalized recommendation technique that provides suggestions to users, using items that such users have consumed in the past in contexts similar to their present contexts.

Context-Aware Personalized Content-Based Collaborative Recommendation: Is a personalized recommendation technique that provides new items to a target user, using items that other users who are similar to the target user have consumed in the contexts similar to the target user’s present context, including (if any) items that the target user has consumed in similar contexts.

Context-Aware Personalized Recommendation: Is a technology that assists users to select from overwhelming set of potential choices by matching and customizing available services to user’s context dependent preferences. It supports the user by providing the right services in the right format at the right moments in order to improve user’s experience.

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