See Clicks Differently: Modeling User Clicking Alternatively with Multi Classifiers for CTR Prediction

Shiwei Lyu, Hongbo Cai, Chaohe Zhang, Shuai Ling, Yue Shen, Xiaodong Zeng, Jinjie Gu, Guannan Zhang, Haipeng Zhang

Abstract: Many recommender systems optimize click through rates (CTRs) as one of their core goals, and it further breaks down to predicting each item's click probability for a user (user-item click probability) and recommending the top ones to this particular user. User-item click probability is then estimated as a single term, and the basic assumption is that the user has different preferences over items. This is presumably true, but from real-world data, we observe that some people are naturally more active in clicking on items while some are not. This intrinsic tendency contributes to their user-item click probabilities. Besides this, when a user sees a particular item she likes, the click probability for this item increases due to this user-item preference. Therefore, instead of estimating the user-item click probability directly, we break it down into two finer attributes: user's intrinsic tendency of clicking and user-item preference. Inspired by studies that emphasize item features for overall enhancements and research progress in multi-task learning, we for the first time design a Multi Classifier Click Rate prediction model (MultiCR) to better exploit item-level information by building a separate classifier for each item. Furthermore, in addition to utilizing static user features, we learn implicit connections between user's item preferences and the often-overlooked indirect user behaviors (e.g., click histories from other services within the app). In a common new-campaign/new-service scenario, MultiCR outperforms various baselines in large-scale offline and online experiments and demonstrates good resilience when the amount of training data decreases.