A Mobile Context-Aware Proactive Recommendation Approach

The prototype implements three algorithms to recommend concerts by taking advan- tage of what users have listened to before: a collaborative filtering algorithm (K-Nearest Neighbor),

More specifically, in context-aware splitting approaches, the percentage of emotional contexts used by item splits or user splits can tell the importance of emotions in

The Adaptation Engine, in order to decide the most appropriate adaptation that should be applied in each specific context, needs to process adaptation rules expressed

To adapt the ε-greedy algorithm to a context aware environ- ment, we propose to compute the similarity between the current situation and each one in the situation

The degree of success on recommendations was then evaluated by the ratio of chosen photos that are in the Gold Standard (e.g., if in a given combination of user and

When, the time-factor is disabled the user will retrieve news solely based on the other relevance factors (location and personal interests). Figure 2b shows an example of how

My PhD thesis goal is to study what kinds of context information there are in a recommender system, how many ways we can obtain this information (implicit, users introduce

Capability metamodel for modeling context-aware recommendations A Recommendation Pattern can be applied automatically (e.g. automatic Software Entity highlighting) or recommended to