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 (i.e., 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 real-world 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 query-document pairs.
['Haitao Yu', 'Adam Jatowt', 'Roi Blanco', 'Hideo Joho', 'Joemon M. Jose']
Decoding multi-click search behavior based on marginal utility