User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on pre- dicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.
There are sporadic works that have been proposed to exploit historical statuses for attrition prediction @cite_36 @cite_28 . Although these works have tried to incorporate historical statuses of users, they have two issues. First, the whole historical observation periods are divided into multiple sub-periods for model training with handcrafted efforts. In this case, the correlation across different sub-periods cannot be fully explored. Second, the decaying impact of statuses within different sub-periods on attrition prediction within the target time period is out of consideration. The survival analysis framework has been proposed to capture the time-to-event of attrition @cite_33 . It utilizes the initial information at the start of the user enrollment to perform model learning for predicting survival time of subscriptions. Here the inherent problem is that the evolving user activities are not incorporated into the attrition prediction, which are crucial to the attrition modeling according to our experiments. Aside from the above works purely based on attrition, profit-driven discussion and simulation studies were also performed based on a potential intervention assumption (e.g., bonus, discount) @cite_7 .
@cite_36 @cite_28 @cite_33 @cite_7
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The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation. Abstract Several competing human behavior models have been proposed to model boundedly rational adversaries in repeated Stackelberg Security Games (SSG). However, these existing models fail to address three main issues which are detrimental to defender performance. First, while they attempt to learn adversary behavior models from adversaries' past actions (“attacks on targets”), they fail to take into account adversaries' future adaptation based on successes or failures of these past actions. Second, existing algorithms fail to learn a reliable model of the adversary unless there exists sufficient data collected by exposing enough of the attack surface – a situation that often arises in initial rounds of the repeated SSG. Third, current leading models have failed to include probability weighting functions, even though it is well known that human beings' weighting of probability is typically nonlinear. To address these limitations of existing models, this article provides three main contributions. Our first contribution is a new human behavior model, SHARP, which mitigates these three limitations as follows: (i) SHARP reasons based on success or failure of the adversary's past actions on exposed portions of the attack surface to model adversary adaptivity; (ii) SHARP reasons about similarity between exposed and unexposed areas of the attack surface, and also incorporates a discounting parameter to mitigate adversary's lack of exposure to enough of the attack surface; and (iii) SHARP integrates a non-linear probability weighting function to capture the adversary's true weighting of probability. Our second contribution is a first “repeated measures study” – at least in the context of SSGs – of competing human behavior models. This study, where each experiment lasted a period of multiple weeks with individual sets of human subjects on the Amazon Mechanical Turk platform, illustrates the strengths and weaknesses of different models and shows the advantages of SHARP. Our third major contribution is to demonstrate SHARP's superiority by conducting real-world human subjects experiments at the Bukit Barisan Seletan National Park in Indonesia against wildlife security experts. The study of career development has become more important during a time of rising competition. Even with the help of newly available big data in the field of human resources, it is challenging to prospect the career development of talents in an effective manner, since the nature and structure of talent careers can change quickly. To this end, in this paper, we propose a novel survival analysis approach to model the talent career paths, with a focus on two critical issues in talent management, namely turnover and career progression. Specifically, for modeling the talent turnover behaviors, we formulate the prediction of survival status at a sequence of time intervals as a multi-task learning problem by considering the prediction at each time interval as a task. Also, we impose the ranking constraints to model both censored and uncensored data, and capture the intrinsic properties exhibited in general lifetime modeling with non-recurrent and recurrent events. Similarly, for modeling the talent career progression, each task concerns the prediction of a relative occupational level at each time interval. The ranking constraints imposed on different occupational levels can help to reduce the prediction error. Finally, we evaluate our approach with several state-of-the-art baseline methods on real-world talent data. The experimental results clearly demonstrate the effectiveness of the proposed models for predicting the turnover and career progression of talents. Time-to-event outcomes based data can be modelled using survival regression methods which can predict these outcomes in different censored data applications in diverse fields such as engineering, economics and healthcare. Predictive models are built by inferring from the censored variable in time-to-event data, which differentiates them from other regression methods. Censoring is represented as a binary indicator variable and machine learning methods have been tuned to account for the censored attribute. Active learning from censored data using survival regression methods can make the model query a domain expert for the time-to-event label of the sampled instances. This offers higher advantages in the healthcare domain where a domain expert can interactively refine the model with his feedback. With this motivation, we address this problem by providing an active learning based survival model which uses a novel model discriminative gradient based sampling scheme. We evaluate this framework on electronic health records (EHR), publicly available survival and synthetic censored datasets of varying diversity. Experimental evaluation against state of the art survival regression methods indicates the higher discriminative ability of the proposed approach. We also present the sampling results for the proposed approach in an active learning setting which indicate better learning rates in comparison to other sampling strategies.