Fair and Balanced: Learning to Present News Stories
Abstract
Relevance, diversity and personalization are key issues when
presenting content which is apt to pique a user's interest.
This is particularly true when presenting an engaging set
of news stories. In this paper we propose an ecient algo-
rithm for selecting a small subset of relevant articles from a
streaming news corpus. It oers three key pieces of improve-
ment over past work: 1) It is based on a detailed model of a
user's viewing behavior which does not require explicit feed-
back. 2) We use the notion of submodularity to estimate
the propensity of interacting with content. This improves
over the classical context independent relevance ranking al-
gorithms. Unlike existing methods, we learn the submodu-
lar function from the data. 3) We present an ecient online
algorithm which can be adapted for personalization, story
adaptation, and factorization models. Experiments show
that our system yields a signicant improvement over a re-
trieval system deployed in production.
presenting content which is apt to pique a user's interest.
This is particularly true when presenting an engaging set
of news stories. In this paper we propose an ecient algo-
rithm for selecting a small subset of relevant articles from a
streaming news corpus. It oers three key pieces of improve-
ment over past work: 1) It is based on a detailed model of a
user's viewing behavior which does not require explicit feed-
back. 2) We use the notion of submodularity to estimate
the propensity of interacting with content. This improves
over the classical context independent relevance ranking al-
gorithms. Unlike existing methods, we learn the submodu-
lar function from the data. 3) We present an ecient online
algorithm which can be adapted for personalization, story
adaptation, and factorization models. Experiments show
that our system yields a signicant improvement over a re-
trieval system deployed in production.