User Response Driven Content Understanding with Causal Inference Content understanding with many potential industrial applications, is spurring interest by researchers in many areas in artificial intelligence. We propose to revisit the content understanding problem in digital marketing from three novel perspectives. First, our problem is to explore the way how user experience is delivered with divergent key multimedia elements. Second, we treat understanding as to elucidate their causal implications in driving user responses. Third, we propose to understand content based on observational audience visit logs. To approach this problem, we measure and generate heterogeneous content features and model them as binary, multivalued or continuous genres. Multiple key performance indicators (KPIs) are introduced to quantify user responses. We then develop a flexible and adaptive doubly robust estimator to identify the causality between these features and user responses from observational data. The comprehensive experiments are performed on realworld data sets. We show that the further analysis of the experimental results can shed actionable insights on how to improve KPIs. Our work will benefit content distribution and optimization in digital marketing.

Unsupervised Qualitative Scoring for Binary Item Features The qualitative score of tags is widely used to describe which product is better in terms of the given property. For example, in a restaurantnavigation site, properties such as food, location, and mood are given in the form of numerical values, representing the goodness of each aspect. In this paper, we propose a novel approach to estimate the qualitative score from the binary features of products. Based on a natural assumption that an item with a better property is more popular among users who prefer that property, in short, experts know best, we introduce one discriminative and two generative models with which user preferences and itemqualitative scores are inferred from useritem interactions. Our approach contributes to resolving the following difficulties: (1) no supervised data for the score estimation, (2) implicit user purpose, and (3) irrelevant tag contamination. We evaluate our models by using two artificial datasets and two realworld datasets of movie and book ratings and observe that our models outperform baseline models.