Effective public service delivery supported by time decayed Bayesian personalized ranking
Abstract Information overload is becoming a prominent problem that hinders the effectiveness of e government. Personalized e government services with recommendation techniques may provide a solution by helping users find the target service items based on her interaction histories. Most existing e government recommendation methods are rating based, but the ratings in e government are unavailable. Besides, the interactions in e government are sequence dependent while the sequential information are usually ignored. These two problems negatively affect the efficiency of the service distribution of e government platforms. In this paper, we studied the sequential implicit feedback recommendation problem in e government and design a novel learning algorithm called time decayed Bayesian personalized ranking. Time decayed BPR captures the sequence effects using time decayed Ordinal Utility and inherits the seminal pairwise learning framework of BPR. It digests users' sequential behavior naturally, and combines the benefits of time aware information (i.e. sequential behavior) and time invariant information (i.e. general taste) An empirical analysis of real world e government datasets shows that our time decayed BPR approach improves the performance significantly regarding various evaluation metrics compared with the state of the art baseline methods.
Knowl. Based Syst.