VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation

One of the most essential parts of any recommender system is personalization– how acceptable the recommendations are from the user’s perspective. However, in many real-world applications, there are other stakeholders whose needs and interests should be ...

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...

Historically, the main criterion for a successful recommender system was the relevance of the recommended items to the user. In other words, the only objective for the recommendation algorithm was to learn user’s preferences for different items and ...