H6 The review question page before rating is omitted and switch to tilt interaction to rate in moving context has a direct positive effect on perceived user satisfaction;..

The fundamental purpose of Context-Aware Recommender Systems consists in combining the user’s context and environment in a same infrastructure to better characterize the

The fundamental purpose of Context-Aware Recommender Systems consists in combining the user’s context and environment in a same infrastructure to better characterize the

the user’s interests in mobile context-aware recommender systems: The hybrid- e-greedy algorithm,” in Advanced Information Networking and Applications Workshops (WAINA), 2012

Our objectives for this new proposition of context factors categorization are to satisfy the needs of CARS, while (1) satisfying the definition of [3], (2) improving the

1 in a device built around an InSb semiconductor nanowire with an NbTiN contact used to induce superconductivity and a normal metal Pd contact used to perform tunneling spectroscopy

Thus, existing recommender systems do not model explicitly user long-term needs that related to the visual appearance of products expressed as “like product X of look-and-feel Y”..

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

– le droit privé règle les rapports des particuliers entre eux (le droit privé correspond au droit civil, au droit du travail, au droit commercial, ...),.. – le

We show, for all of the proposed approaches, how the RS performance evolves over time as more adaptive learning is done and we prove the interest of considering the drifts and

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

Another issue we look at is how to best input sequence-level information s (u) , by fusing it at each time step with the items along with the token-level features, or by embedding

Therefore, smart recommender systems that accelerate content access with personalized suggestions closely matching user interests can considerably improve the mobile media

In this paper we empirically compare the three contextual approaches, i.e., pre-filtering, post-filtering and contextual modeling among themselves to deter-

• From the aspect of volume, this dataset is able to measure the performance of the three context-aware collaborator recommendation algorithms (i.e., PreF1, PoF1, and PoF2),

Travelling recommendation systems have become widely used in organizing and planning touristic trips. One of the main issues of such systems is the maintenance of the points

When comparing the pre-, the post-filtering and the contextual modeling methods, we used the two post-filtering approaches (Weight and Filter), the exact

- Générer un rapport pouvant mesurer, par mission et par titre d’emploi, le nombre de déplacements d’employés entre deux installations de type milieu de vie