Exploiting multiple action types in recommender systems
Implicit feedback recommender systems provide personalized suggestions for items that are predicted to be of interest to the user, by collecting online users' activity and inferring from it users' preferences. While the state-of-the-art models are built by employing observations of user actions of one single type, the usage of multiple action types enables to observe more information and to build more effective recommenders. This paper presents an ongoing research in the eld of implicit feedback recommender systems employing information about multiple action types. It highlights the peculiarities of existing models and identi es the research questions that should be solved to build solutions that can exploit multiple action types.