Web user profiles with time-decay and prototyping

摘要

User profiling represents an important initial step in personalizing web services and in building recommendation systems. Non-invasive profiling methods monitor users' behavior and infer interest profiles from their past actions. Most existing profiling methods, which relate the users' interests to a given ontology, consider only the user's past actions when calculating his/her profile. The profiling algorithms use a time-decay function for users' past actions to adapt the profile to shifts in the user's interests over time. In our work, we propose a hybrid method that combines time-decay and profile correction using prototype profiles. The additional profile correction step considers the interests of similar users and expands the interest scores beyond the concepts detected in the user's past actions, which facilitates faster profile adaptation to the user's new interests. In our experimental work, we experimented extensively with two real data sets: data of an online advertising network and student data in an online e-learning environment. We measured the quality of the computed user profiles by correlating them to users' future actions. Experiments revealed that it is crucial to build the user's profile using a large number of events from his/her past and to update the profile regularly. When we are unable to do so, the profile correction can be used to keep the quality of the profile from dropping too low. The results show that our method significantly outperforms existing ontological profiling methods.