Recommender systems are powerful tools that support the user in their quest to find the multimedia they are looking for. Such systems present multimedia contents or provide recommendations by taking into consideration two dimensions of inputs: content (item), and user (consumer). Little attention has been paid to increasing the quality of the experience by understanding the contextual aspect of the user when he/she wants to consume multimedia content.

Given today's mobile and smart devices, and the ability to access different multimedia contents in real-time, it is difficult for users to find the right multimedia content from such a large number of choices. Users also consume diverse multimedia based on many contexts, with different personal preferences and settings. For these reasons, there is a need to reinforce recommendation process with context-adaptive information that can be used to select the right multimedia content and deliver the recommendations in preferred mechanisms.

Ambient intelligence environments are technologically augmented surroundings that aim to provide personalized services to the users based on their context. Identifying these services for the users has become an increasingly challenging task. The overwhelming number of services in the ambient environment has made the selection and management of services even more challenging.

In recent years, users of ambient intelligence environments have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help users in ambient environment get relevant media tailored to their interests, we propose a new method which adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging services. We model the ternary relations among user, resource and tag as a weighted, undirected tripartite graph.