A Context-Aware Framework for Media Recommendation on Smartphones

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.

Section: Methodsmentioning

confidence: 99%

Section: Learning Contextual User Profilementioning

confidence: 99%

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

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Context-Aware Personalization Using Neighborhood-Based Context Similarity

2016

Wireless Pers Commun

Context-Aware Personalization for Mobile Services

Encyclopedia of Information Science and Technology, Fourth Edition

2018

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.

Context-Aware Personalization for Mobile Services

Advances in Computer and Electrical Engineering

2019

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. The contribution is mobile context-aware media personalization (MobCAMP), which is a special kind of personalization that utilizes users' contexts and activities to suggest media content according to the user's tastes and contextual situations.