{'fraction_non_alphanumeric': 0.015138023152270703, 'fraction_numerical': 0.0, 'mean_word_length': 5.690476190476191, '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}}
A semantic approach to avoiding fake neighborhoods in collaborative recommendation of coupons through digital TV

Consumers are flooded with amounts of discount coupons, oftentimes for products that are far from their interests. This marketing custom is already rising on the Internet and is imminent in Digital TV, where the massive sending of coupons leads to their devaluation and consumer indifference. The computing capabilities of these media permit to alleviate this problem by means of recommender systems, which are very useful tools in application domains that suffer from information overload. However, current recommender systems overlook the diversity of products and services available in the market, which gives rise to forming fake neighborhoods in collaborative filtering strategies. In this paper, we apply semantic reasoning techniques to avoid such fake neighborhoods and, thereby, improve the recommendation process. Furthermore, taking advantage of the Digital TV medium, we propose matching the recommended coupons to TV contents semantically related with them, in order to increase their redemption.