{'fraction_non_alphanumeric': 0.012879484820607176, 'fraction_numerical': 0.0, 'mean_word_length': 5.252873563218391, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Approximately optimal facet selection

Multifaceted search is a popular interaction paradigm that allows users to analyze and navigate through multidimensional data. A crucial aspect of faceted search applications is selecting the list of facets to display to the user following each query. We call this the facet selection problem.
 When refining a query by drilling down into a facet, documents that are associated with that facet are promoted in the rankings, as better-ranking documents not associated with the facet are filtered out. We formulate facet selection as an optimization problem aiming to maximize the rank promotion of certain documents. As the optimization problem is NP-Hard, we propose an approximation algorithm for selecting an approximately optimal set of facets per query.
 We conducted experiments over hundreds of queries and search results of a large commercial search engine, comparing two flavors of our algorithm to facet selection algorithms appearing in the literature. The results show that our algorithm significantly outperforms those baseline schemes.