{'fraction_non_alphanumeric': 0.010704727921498661, 'fraction_numerical': 0.0, 'mean_word_length': 5.338983050847458, '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}}
Optimizing Display Advertisements Based on Historic User Trails

Effective online display advertising requires a dynamic selection of the advertisement to be displayed when a web page is fetched. As the goal of displaying advertisement is to engage the users and obtain clicks, the advertisement which has the highest probability of click should be displayed. In this paper we address the problem of finding the most suitable display advertisement option for a user given his/her current browsing session. Using this historical browsing session information, we mine the association of different advertisement views, engagements and clicks, and apply Bayesian models to find the likelihood of an advertisement to be clicked given a specific set of events that describe a user session. A major challenge in training the model for optimum precision is the sparsity of click events, hence we propose the use of advertisement engagement as a success event like clicks to train the model more effectively. Our technique significantly outperforms the baseline technique of using prior probabilities for selecting advertisements.