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User response prediction, which models the user preference w.r.t. thepresented items, plays a key role in online services. With two-decade rapiddevelopment, nowadays the cumulated user behavior sequences on mature Internetservice platforms have become extremely long since the user's firstregistration. Each user not only has intrinsic tastes, but also keeps changingher personal interests during lifetime. Hence, it is challenging to handle suchlifelong sequential modeling for each individual user. Existing methodologiesfor sequential modeling are only capable of dealing with relatively recent userbehaviors, which leaves huge space for modeling long-term especially lifelongsequential patterns to facilitate user modeling. Moreover, one user's behaviormay be accounted for various previous behaviors within her whole onlineactivity history, i.e., long-term dependency with multi-scale sequentialpatterns. In order to tackle these challenges, in this paper, we propose aHierarchical Periodic Memory Network for lifelong sequential modeling withpersonalized memorization of sequential patterns for each user. The model alsoadopts a hierarchical and periodical updating mechanism to capture multi-scalesequential patterns of user interests while supporting the evolving userbehavior logs. The experimental results over three large-scale real-worlddatasets have demonstrated the advantages of our proposed model withsignificant improvement in user response prediction performance against thestate-of-the-arts.

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction