Incorporating Vision Bias into Click Models for Image-oriented Search
  Engine
  Most typical click models assume that the probability of a document to be
examined by users only depends on position, such as PBM and UBM. It works well
in various kinds of search engines. However, in a search engine where massive
candidate documents display images as responses to the query, the examination
probability should not only depend on position. The visual appearance of an
image-oriented document also plays an important role in its opportunity to be
examined. In this paper, we assume that vision bias exists in an image-oriented
search engine as another crucial factor affecting the examination probability
aside from position. Specifically, we apply this assumption to classical click
models and propose an extended model, to better capture the examination
probabilities of documents. We use regression-based EM algorithm to predict the
vision bias given the visual features extracted from candidate documents.
Empirically, we evaluate our model on a dataset developed from a real-world
online image-oriented search engine, and demonstrate that our proposed model
can achieve significant improvements over its baseline model in data fitness
and sparsity handling.
