← all papers · overview

Dynamic Adaptation of User Preferences and Results in a Destination Recommender System

Abstract

Studying human factors has gained a lot of interest in recommender systems research recently. User experience plays a vital role in tourism recommender systems since user satisfaction is the main factor that guarantees the success of such recommender systems. In this work, we have designed and implemented a destination recommender system in which the recommendations adapt instantly based on the user preferences. The recommendations can be explored on a world map with additional information. This interface addresses common visualization challenges in recommender systems, such as transparency, justification, controllability, explorability, the cold-start problem, and context awareness. We have conducted a user study to evaluate different aspects of this recommender system from the users' perspective.

Related papers

Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).