Decoding multiclick search behavior based on marg

Authors: HaiTao Yu Adam Jatowt Roi Blanco Hideo Joho Joemon M Jose

Publish Date: 2016/10/22

Volume: 20, Issue: 1, Pages: 25-52

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Abstract

Query logs contain rich feedback information from users interacting with search engines Therefore various click models have been developed to interpret users’ search behavior and to extract useful knowledge from query logs However most existing models are not designed to consider novelty bias in click behavior The underlying hypothesis behind this paper is that given the previously clicked documents a user tends to choose documents which provide novel relevant information to satisfy her information need rather than redundant relevant information Moreover the prior click models have been mainly tested on frequently occurring queries hence leaving a large proportion of sparse queries uncovered In this paper we propose to predict users’ click behavior from the perspective of utility theory ie utility and marginal utility In particular as a complement to the examination hypothesis we introduce a new hypothesis called marginal utility hypothesis to characterize the effect of novelty bias on users’ click behavior by exploring the semantic divergence among documents in a result list Moreover to cope with sparse or unseen queries that have not been observed in the training set we use a set of descriptive features to quantify the probability of a document being relevant and probability of a document providing marginally novel useful information Finally a series of experiments are conducted on a realworld data set to validate the effectiveness of the proposed methods The experimental results verify the effectiveness of interpreting users’ click behavior based on the marginal utility hypothesis especially when query sessions contain sparse queries or unseen querydocument pairs

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