Algorithms and System Architecture for Immediate Personalized News
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by
Takeshi Yoneda, Shunsuke Kozawa, Keisuke Osone, Yukinori Koide, Yosuke
Abe, Yoshifumi Seki
2019
Abstract
Personalization plays an important role in many services, just as news does.
Many studies have examined news personalization algorithms, but few have
considered practical environments. This paper provides algorithms and system
architecture for generating immediate personalized news in a practical
environment. Immediacy means changes in news trends and user interests are
reflected in recommended news lists quickly. Since news trends and user
interests rapidly change, immediacy is critical in news personalization
applications. We develop algorithms and system architecture to realize
immediacy. Our algorithms are based on collaborative filtering of user clusters
and evaluate news articles using click-through rate and decay scores based on
the time elapsed since the user's last access. Existing studies have not fully
discussed system architecture, so a major contribution of this paper is that we
demonstrate a system architecture and realize our algorithms and a
configuration example implemented on top of Amazon Web Services. We evaluate
the proposed method both offline and online. The offline experiments are
conducted through a real-world dataset from a commercial news delivery service,
and online experiments are conducted via A/B testing on production
environments. We confirm the effectiveness of our proposed method and also that
our system architecture can operate in large-scale production environments.
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