Fair and balanced: Learning to present news stories
Proceedings of the fifth ACM international conference on Web search and data …, 2012•dl.acm.org
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 efficient algorithm for selecting a small subset of
relevant articles from a streaming news corpus. It offers three key pieces of improvement
over past work: 1) It is based on a detailed model of a user's viewing behavior which does
not require explicit feedback. 2) We use the notion of submodularity to estimate the …
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 efficient algorithm for selecting a small subset of
relevant articles from a streaming news corpus. It offers three key pieces of improvement
over past work: 1) It is based on a detailed model of a user's viewing behavior which does
not require explicit feedback. 2) We use the notion of submodularity to estimate the …
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 efficient algorithm for selecting a small subset of relevant articles from a streaming news corpus. It offers three key pieces of improvement over past work: 1) It is based on a detailed model of a user's viewing behavior which does not require explicit feedback. 2) We use the notion of submodularity to estimate the propensity of interacting with content. This improves over the classical context independent relevance ranking algorithms. Unlike existing methods, we learn the submodular function from the data. 3) We present an efficient online algorithm which can be adapted for personalization, story adaptation, and factorization models. Experiments show that our system yields a significant improvement over a retrieval system deployed in production.
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