Abstract

In many applications, users specify target values for certain attributes/features, without requiring exact matches to these values in return. Instead, the result is typically a ranked list of "top k" objects that best match the specified feature values. User subjectivity is an important aspect of such queries i.e. which objects are relevant to the user and which are not depends on the perception of the user. Due to the subjective nature of top-k queries, the answers returned by the system to an user query often does not satisfy the user's need right away; either because the weights and the distance functions associated with the features do not accurately capture the user's perception or because the specified target values do not fully capture her information need or both. In such cases, the user would like to refine the query and resubmit it in order to get back a better set of answers. While there has been a lot of research on query refinement models, there is no work that we ...

Citations