{'fraction_non_alphanumeric': 0.02184235517568851, 'fraction_numerical': 0.0, 'mean_word_length': 5.057471264367816, '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}}
Mining Revenue-Maximizing Bundling Configuration

With greater prevalence of social media, there is an increasing amount of user-generated data revealing consumer preferences for various products and services. Businesses seek to harness this wealth of data to improve their marketing strategies. Bundling, or selling two or more items for one price is a highly-practiced marketing strategy. In this paper, we address the bundle configuration problem from the data-driven perspective. Given a set of items in a seller's inventory, we seek to determine which items should belong to which bundle so as to maximize the total revenue, by mining consumer preferences data. We show that this problem is NP-hard when bundles are allowed to contain more than two items. Therefore, we describe an optimal solution for bundle sizes up to two items, and propose two heuristic solutions for bundles of any larger size. We investigate the effectiveness and the efficiency of the proposed algorithms through experimentations on real-life rating-based preferences data.