Comprehension-based result snippets

Result snippets are used by most search interfaces to preview query results. Snippets help users quickly decide the relevance of the results, thereby reducing the overall search time and effort. Most work on snippets have focused on text snippets for Web pages in Web search. However, little work has studied the problem of snippets for structured data, e.g., product catalogs. Furthermore, all works have focused on the important goal of creating informative snippets, but have ignored the amount of user effort required to comprehend, i.e., read and digest, the displayed snippets. In particular, they implicitly assume that the comprehension effort or cost only depends on the length of the snippet, which we show is incorrect for structured data. We propose novel techniques to construct snippets of structured heterogeneous results, which not only select the most informative attributes for each result, but also minimize the expected user effort (time) to comprehend these snippets. We create a comprehension model to quantify the effort incurred by users in comprehending a list of result snippets. Our model is supported by an extensive user-study. A key observation is that the user effort for comprehending an attribute across multiple snippets only depends on the number of unique positions (e.g., indentations) where this attribute is displayed and not on the number of occurrences. We analyze the complexity of the snippet construction problem and show that the problem is NP-hard, even when we only consider the comprehension cost. We present efficient approximate algorithms, and experimentally demonstrate their effectiveness and efficiency.