Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities. We also provide experimental evidence to support our theoretical analysis. We propose two simple but effective methods inspired by the fixpoint computation in similarity flooding, and demonstrate their effectiveness on benchmark datasets. Our work bridges the gap between recent embedding-based models and the conventional similarity flooding algorithm. It would improve our understanding of and increase our faith in embedding-based EA.

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources may damage the completion accuracy. In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. Specifically, we introduce a graph neural network with a holistic scoring function to judge the plausibility of facts with various value types. We design value alignment networks to resolve the heterogeneity between values and map them to entities even outside the KG. Furthermore, we present a truth inference model that incorporates data source qualities into the fact scoring function, and design a semi-supervised learning way to infer the truths from heterogeneous values. We conduct extensive experiments to compare our method with the state-of-the-arts. The results show that our method achieves superior accuracy not only in completing missing facts but also in discovering new facts.

The e-commercial search engine is the primary gateway for customers to find desired products and engage in online shopping. Besides displaying items to optimize for a single objective (i.e., relevance), ranking items needs to satisfy some other business requirements in practice. Recently, traffic shaping was introduced to incorporate multiple objectives in a constrained optimization framework. However, many practical business requirements can not explicitly represented by linear constraints as in the existing work, and this may limit the scalablity of their framework. This paper presents a unified framework from the aspect of multi-objective welfare maximization where we regard all business requirements as objectives to optimize. Our framework can naturally incorporate a wide range of application-driven requirements. In addition to formulating the problem, we design an online traffic splitting algorithm that allows us to flexibly adjust the priorities of different objectives, and it has rigorous theoretical guarantees over the adversarial scenario. We also run experiments on both synthetic and real-world datasets to validate our algorithms.

Click-through rate (CTR) prediction is a core task in the field of recommender system and many other applications. For CTR prediction model, personalization is the key to improve the performance and enhance the user experience. Recently, several models are proposed to extract user interest from user behavior data which reflects user's personalized preference implicitly. However, existing works in the field of CTR prediction mainly focus on user representation and pay less attention on representing the relevance between user and item, which directly measures the intensity of user's preference on target item. Motivated by this, we propose a novel model named Deep Match to Rank (DMR) which combines the thought of collaborative filtering in matching methods for the ranking task in CTR prediction. In DMR, we design User-to-Item Network and Item-to-Item Network to represent the relevance in two forms. In User-to-Item Network, we represent the relevance between user and item by inner product of the corresponding representation in the embedding space. Meanwhile, an auxiliary match network is presented to supervise the training and push larger inner product to represent higher relevance. In Item-to-Item Network, we first calculate the item-to-item similarities between user interacted items and target item by attention mechanism, and then sum up the similarities to obtain another form of user-to-item relevance. We conduct extensive experiments on both public and industrial datasets to validate the effectiveness of our model, which outperforms the state-of-art models significantly.

Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper coupons which provide customers with discounts or gifts. One of the fundamental problems related is how to deliver e-coupons with minimal cost while users' willingness to place an order is maximized. We call this problem the coupon allocation problem. This is a non-trivial problem since the number of regular users on a mature e-platform often reaches hundreds of millions and the types of e-coupons to be allocated are often multiple. The policy space is extremely large and the online allocation has to satisfy a budget constraint. Besides, one can never observe the responses of one user under different policies which increases the uncertainty of the policy making process. Previous work fails to deal with these challenges. In this paper, we decompose the coupon allocation task into two subtasks: the user intent detection task and the allocation task. Accordingly, we propose a two-stage solution: at the first stage (detection stage), we put forward a novel Instantaneous Intent Detection Network (IIDN) which takes the user-coupon features as input and predicts user real-time intents; at the second stage (allocation stage), we model the allocation problem as a Multiple-Choice Knapsack Problem (MCKP) and provide a computational efficient allocation method using the intents predicted at the detection stage. Long Short Term Memory (LSTM) and a special attention mechanism are applied on IIDN to better describe temporal dependencies of sequential features. And we manage to solve the imbalanced label problem for the user intent detection task with a brand new perspective by using the logical relationship between multiple user intents. We conduct extensive online and offline experiments and the results show the superiority of our proposed framework, which has brought great profits to the platform and continues to function online.

Nowadays, online shoppers have paid more and more attention to detailed product descriptions, since a well-written description is a huge factor in making online sales. However, for a website with billions of product data like Alibaba, the writing efficiency of human copywriters cannot match the growth rate of new products. To address this issue, we propose a novel pointer-generator neural network to generate product description. In particular, coordinate encoders and a pattern-controlled decoder are utilized to improve generation quality with an attention mechanism. The coordinate encoders equipped with a Transformer and a gated convolutional unit is introduced to learn the source input representations. In the decoding phase, a pattern controlled decoder is proposed to control the output description pattern (such as category, length, and style) to ensure the quality of the description. For evaluation, we build a substantial collection of real-world products along with human-written descriptions. An extensive set of experiments with both human annotated data demonstrate the advantage of the proposed method for generation qualities. Finally, an online deployment shows significant benefits of our model in a real online shopping scenario, as measured by the click-through rate.