Long Term Recommender Benchmarking for Mobile Shopping List Applications using Markov Chains

Thus, the main contributions of our paper are: (a) a novel interaction design that is used to elicit long-term (general) and short-term (session-based) user preferences; and (b)

Thus, the system performs the simulated annealing algorithm using this energy function and a user profile, so that it can provide a combination of tourism

Here the underlying proposition is that a classier is likely to predict the label of the object from a test set correctly if it has correctly classied its neighbors from a

One is to study whether a mobile recommender model with interactive explanations leads to more user con- trol and transparency in critique-based mobile recommender systems.. Second

We blend algorithms and user interface design to present a multi-list recommender interface that presents multiple lists of personalized items in a single interface, where each list

While this has been shown to work well under certain circumstances, it sometimes seems not pos- sible to assess user experience without enabling users to consume items, raising

E.g., if Interstellar is the initial item, Stanley Kubrick was annotated in one of its reviews, and 2001: A Space Odyssey was discovered through Stanley Kubrick, then 2001: A

Our approach allows the user to control the contextual information, i.e., the user can define the content (other users and items) and parameters (users, items, novelty and