The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation
BEIDOU WANG*, Simon Fraser University; Martin Ester, Simon Fraser University; Yikang Liao, Zhejiang University; Jiajun Bu, Zhejiang University; Yu Zhu, Zhejiang University; Deng Cai, ; Ziyu Guan,
With email overload becoming a billion-level drag on the economy, personalized email prioritization is of urgent need to help predict the importance level of an email. Despite lots of previous eﬀort on the topic, broadcast email, an important type of emails with its unique challenges and intriguing opportunities, has been overlooked. The most salient opportunity lies in that eﬀective collaborative ﬁltering can be exploited due to thousands of receivers of a typical broad-cast email. However, every broadcast email is completely “cold” and it is very costly to obtain users’ preference feed-back. Fortunately, there exist up to million-level broadcast mailing lists in a real life email system. Similar mailing lists can provide useful extra information for broadcast email prioritization in a target mailing list. How to mine such useful extra information is a challenging problem that has never been touched. In this work, we propose the ﬁrst broadcast email prioritization framework considering large numbers of mailing lists by formulating this problem as a cross domain recommendation problem. An optimization framework is proposed to select the optimal set of source domains considering multiple criteria including overlap of users, feedback pattern similarity and coverage of users. Our method is thoroughly evaluated on a real world industrial dataset from Samsung Electronics and is proved highly eﬀective and out-performs all the baselines.
Filed under: Recommender Systems