It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi-Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large realworld dataset that our proposed model is superior to stateof-the-art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.Keywords computational advertising, multi-touch attribution, survival analysis INTRODUCTIONInternet increasingly becomes the leading advertising medium, where online users generate a tremendous amount of feedback information including clicks and conversions. The feedback data reveal the needs/preferences of users, and thus enable online advertising systems to deliver ads to those who are most likely to respond. Nowadays companies spare no effort to attract consumers to visit their websites through various advertising channels, among which display ads and search ads are two dominant types.Recently, researchers from both academia and industry have become more and more interested in analysing the contribution of each advertising channel to user conversion which is known as the "attribution" problem. An accurate attribution model would be of great help for advertisers to interpret the effects of different advertising channels and make informed decisions to optimize their advertising campaigns (e.g., by reallocating advertising budgets). An online advertising campaign is usually launched across multiple channels such as display ads, paid search ads, social media ads, and so on. In most cases, users would have been exposed to the ads from a particular advertising campaign many times before their final conversion, as illustrated in Figure 1. Suppose that a brand X delivers ads through three channels: display, social and paid search: user 1 saw X's display ad at t 1 1 when browsing a webpage, and then saw X's social ad at t 1 2 ; later, she searched for X's products and clicked its paid ad link at t 1 3 ; finally, she made a purchase on X's website at time T 1 . In this case, how should we assess the contribution of those three advertising channels to that user's conversion?A number of attribution models have been proposed and utilized in recent years. Figure 2 shows some representative ones. Most of the existing attribution models widely used in practice are rule-based, and their effectivenesses are limited by thei...

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Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still face several challenges: (1) Behaviors are much more complex than words in sentences, so traditional attentive and recurrent models have limitations capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is di cult to integrate long-term memory and short-term intent. In this paper, we propose a temporal gating methodology to improve attention mechanism and recurrent units, so that temporal information can be considered in both information ltering and state transition. Additionally, we propose a hybrid sequential recommender, named Multi-hop Time-aware Attentive Memory network (MTAM), to integrate long-term and short-term preferences. We use the proposed time-aware GRU network to learn the short-term intent and maintain prior records in user memory. We treat the short-term intent as a query and design a multi-hop memory reading operation via the proposed time-aware attention to generate user representation based on the current intent and longterm memory. Our approach is scalable for candidate retrieval tasks and can be viewed as a non-linear generalization of latent factorization for dot-product based Top-K recommendation. Finally, we conduct extensive experiments on six benchmark datasets and the experimental results demonstrate the e ectiveness of our MTAM and temporal gating methodology.

Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in Chinese society and receives increasing attention worldwide. The existing herb recommenders learn the complex relations between symptoms and herbs by mining the TCM prescriptions. Given a set of symptoms, they will provide a set of herbs and explanations from the TCM theory. However, the foundation of TCM is Yinyangism (i.e. the combination of Five Phases theory with Yin-yang theory), which is very different from modern medicine philosophy. Only recommending herbs from the TCM theory aspect largely prevents TCM from modern medical treatment. As TCM and modern medicine share a common view at the molecular level, it is necessary to integrate the ancient practice of TCM and standards of modern medicine. In this paper, we explore the underlying action mechanisms of herbs from both TCM and modern medicine, and propose a Meta-path guided Graph Attention Network (MGAT) to provide the explainable herb recommendations. Technically, to translate TCM from an experience-based medicine to an evidence-based medicine system, we incorporate the pharmacology knowledge of modern Chinese medicine with the TCM knowledge. We design a meta-path guided information propagation scheme based on the extended knowledge graph, which combines information propagation and decision process. This scheme adopts meta-paths (predefined relation sequences) to guide neighbor selection in the propagation process. Furthermore, the attention mechanism is utilized in aggregation to help distinguish the salience of different paths connecting a symptom with a herb. In this way, our model can distill the long-range semantics along meta-paths and generate fine-grained explanations. We conduct extensive experiments on a public TCM dataset, demonstrating comparable performance to the state-of-the-art herb recommendation models and the strong explainability.