{'fraction_non_alphanumeric': 0.02888700084961767, 'fraction_numerical': 0.0, 'mean_word_length': 5.4021739130434785, '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}}
Dynamic News Recommendation with Hierarchical Attention Network

News recommendation is an effective information dissemination solution in modern society. In general, news articles can be modeled from multiple granularities: sentence-, element-and news-level. However, the first two levels have been largely ignored in existing methods and it is also unclear how such multi-granularity modeling can enhance news recommendation. In this paper, we propose a novel dynamic model for news recommendation. A unique perspective of our model is to discriminate the contributions of previously interacted contents for triggering the next news-reading, in sentence-, element-and news-level simultaneously. To this end, we design a hierarchical attention network of which the lower layer learns the impacts of sentences and elements, while the upper layer captures disparity of news. Moreover, we incorporate a time-decaying factor to reflect the dynamism, as well as convolution neural networks for learning sequential influence. Using three real-world datasets, we conduct extensive experiments to verify the superiority of our model, compared with several state-of-the-art approaches.