Time feature selection for identifying active household members
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: CIKM '12: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription ABSTRACT Popular online rental services such as Netflix and MoviePilot often manage household accounts. A household account is usually shared by various users who live in the same house, but in general does not provide a
... es not provide a mechanism by which current active users are identified, and thus leads to considerable difficulties for making effective personalized recommendations. The identification of the active household members, defined as the discrimination of the users from a given household who are interacting with a system (e.g. an ondemand video service), is thus an interesting challenge for the recommender systems research community. In this paper, we formulate the above task as a classification problem, and address it by means of global and local feature selection methods and classifiers that only exploit time features from past item consumption records. The results obtained from a series of experiments on a real dataset show that some of the proposed methods are able to select relevant time features, which allow simple classifiers to accurately identify active members of household accounts.