Situation Assessment for Non-Intrusive Recommendation

Moreover, by using the user feedback (selected songs from the recommendation list) to personalize the recommenda- tion model, increased the number of adequate songs for each activity

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),

Our objective is to handle the adaptation of the UI to the context of use (platform, environment, user). Vale [22] describes a parameterized transformation

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

Since we are using an ex- tractive summary procedure, we make use of a classical loss used for summarization systems: we use a recall-oriented ROUGE-n metrics, by comparing

We designed two different interfaces with three elements: A modifiable slider for goal selection showing a recommendation range, default value and colour indicator for probability

In this paper, we present two user-controllable hybrid recommender interfaces, Relevance Tuner (for people recommendation) and Paper Tuner (for paper recommendation), which offer a

(ii) User based photo frequency (PF U ): Given a set of travel history of a user u, the system determines the popular- ity of that category of POI for the user using average