scispace - formally typeset
Search or ask a question
Author

Mingyuan Tao

Bio: Mingyuan Tao is an academic researcher from Alibaba Group. The author has contributed to research in topics: Graph (abstract data type) & Relation (database). The author has co-authored 2 publications.

Papers
More filters
Proceedings ArticleDOI
11 Jul 2021
TL;DR: Wang et al. as discussed by the authors proposed a novel Deep User Match Network (DUMN) which measures the user-to-user relevance for CTR prediction by matching the target user and those who have interacted with candidate item and modeling their similarities in user representation space.
Abstract: Click-through rate (CTR) prediction is a crucial task in many applications (e.g. recommender systems). Recently deep learning based models have been proposed and successfully applied for CTR prediction by focusing on feature interaction or user interest based on the item-to-item relevance between user behaviors and candidate item. However, these existing models neglect the user-to-user relevance between the target user and those who like the candidate item, which can reflect the preference of target user. To this end, in this paper, we propose a novel Deep User Match Network (DUMN) which measures the user-to-user relevance for CTR prediction. Specifically, in DUMN, we design a User Representation Layer to learn a unified user representation which contains user latent interest based on user behaviors. Then, User Match Layer is designed to measure the user-to-user relevance by matching the target user and those who have interacted with candidate item and modeling their similarities in user representation space. Extensive experimental results on three public real-world datasets validate the effectiveness of DUMN compared with state-of-the-art methods.

14 citations

Proceedings ArticleDOI
14 Aug 2021
TL;DR: Zhang et al. as discussed by the authors proposed a Deep Inclusion Relation-aware Network (DIRN) for user response prediction by synthetically exploiting inclusion relations among travel items, which can extract user latent interest with user behaviors in two ways.
Abstract: User response prediction plays a crucial role in many applications (e.g. search ranking and personalized recommendation) at online travel platforms. Although existing methods have made a great success by focusing on feature interaction or user behaviors, they cannot synthetically exploit item inclusion relations describing relationships of an item including or being included by another one, which are important components among travel items. To this end, in this paper, we propose a novel Deep Inclusion Relation-aware Network (DIRN) for user response prediction by synthetically exploiting inclusion relations among travel items. Specifically, on the item graph constructed with inclusion relations, we first leverage a node embedding approach to learn the item graph-based embedding. Then, we design Representation-based Interest Layer and Relation Path Interest Layer to extract user latent interest with user behaviors in two ways. Representation-based Interest Layer models the item-to-item similarity based on item representations containing the graph-based embedding with an attention mechanism and obtains user temporal interest by summing up representations of interacted items with similarities. Relation Path Interest Layer measures item-to-item realistic associations to extract user interest with inclusion relation paths. Offline experiments on a real-world data from Fliggy clearly validate the effectiveness of DIRN. Furthermore, DIRN has been successfully deployed online in search ranking at Fliggy and achieves significant improvement.

5 citations


Cited by
More filters
Proceedings ArticleDOI
06 Jul 2022
TL;DR: A transformer-based multi-representational item network consisting of a multi-CLS representation sub module and contextualized global item representation submodule is proposed and it is proposed to decouple the time information and item behavior to avoid information overwhelming.
Abstract: Click-through rate (CTR) prediction is essential in the modelling of a recommender system. Previous studies mainly focus on user behavior modelling, while few of them consider candidate item representations. This makes the models strongly dependent on user representations, and less effective when user behavior is sparse. Furthermore, most existing works regard the candidate item as one fixed embedding and ignore the multi-representational characteristics of the item. To handle the above issues, we propose a Deep multi-Representational Item NetworK (DRINK) for CTR prediction. Specifically, to tackle the sparse user behavior problem, we construct a sequence of interacting users and timestamps to represent the candidate item; to dynamically capture the characteristics of the item, we propose a transformer-based multi-representational item network consisting of a multi-CLS representation submodule and contextualized global item representation submodule. In addition, we propose to decouple the time information and item behavior to avoid information overwhelming. Outputs of the above components are concatenated and fed into a MLP layer to fit the CTR. We conduct extensive experiments on real-world datasets of Amazon and the results demonstrate the effectiveness of the proposed model.

8 citations

Journal ArticleDOI
TL;DR: Adaptive mixture of experts (AdaMoE) is proposed, a new framework to alleviate the concept drift problem by adaptive filtering in the data stream of CTR prediction, which significantly outperforms all incremental learning frameworks considered.
Abstract: Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with high-speed user-generated data streams, whose underlying distribution rapidly changing over time. The concept drift problem inevitably exists in those streaming data, which can lead to performance degradation due to the timeliness issue. To ensure model freshness, incremental learning has been widely adopted in real-world production systems. However, it is hard for the incremental update to achieve the balance of the CTR models between the adaptability to cap-ture the fast-changing trends and generalization ability to retain common knowledge. In this paper, we propose adaptive mixture of experts (AdaMoE), a new framework to alleviate the concept drift problem by adaptive filtering in the data stream of CTR prediction. The extensive experiments on the offline industrial dataset and online A/B tests show that our AdaMoE significantly outperforms all incremental learning frameworks considered.

6 citations

Proceedings ArticleDOI
01 May 2022
TL;DR: A novel Price Competitiveness-aware Network (PCNet) is proposed to predict users' purchase behaviors by explicitly considering the price competitiveness of items and leverage prior knowledge discovered from a real-world dataset to guide module designs, thus enhancing the performance and interpretability of the PCNet.
Abstract: Price, a crucial factor determining whether a user will purchase an item, has attracted considerable attention in personalized ranking and recommendation. Existing studies commonly assume that only item price affects user online purchase decisions. However, in reality, users not only focus on the price of an item itself but also compare the price with the item's “comparison prices,” including its past prices, prices of similar items, and prices on other e-commerce platforms. Without carefully considering these comparison prices, methods fail to capture the purchase motivation attributable to prices comprehensively. To address this problem, in this paper, we introduce the concept of item price competitiveness. An item's price competitiveness measures the advantage of the item's price over its comparison prices. Then, a novel Price Competitiveness-aware Network (PCNet) is proposed to predict users' purchase behaviors by explicitly considering the price competitiveness of items. Specifically, PCNet consists of three key modules, and each module exploits one corresponding facet of price competitiveness. We leverage prior knowledge discovered from a real-world dataset to guide module designs, thus enhancing the performance and interpretability of the PCNet. Offline experiments show the superiority of the PCNet and verify the effectiveness of each module. Moreover, PCNet has been deployed online in a hotel search engine at Fliggy and benefits both the platform and users.

2 citations

Journal ArticleDOI
Lei Chen, Jie Cao, Weichao Liang, Jia Wu, Qiaolin Ye 
TL;DR: In this paper , a Keywords-enhanced Deep Reinforcement Learning model (KDRL) framework is proposed to learn the travel recommendation and keywords generation simultaneously. But the authors focus on the click behavior and ignore the informative keywords of the clicked products.
Abstract: Tourism is an important industry and a popular entertainment activity involving billions of visitors per annum. One challenging problem tourists face is identifying satisfactory products from vast tourism information. Most of travel recommendation methods regard the recommendation procedure as a static process and only focus on immediate rewards. Meanwhile, they often infer user intensions from click behaviors and ignore the informative keywords of the clicked products. To this end, in this article, we present a Keywords-enhanced Deep Reinforcement Learning model (KDRL) framework. Specifically, we formalize travel recommendation as a Markov Decision Process and implement it upon the Actor–Critic framework. It integrates keyword information into the reinforcement learning–(RL) based recommendation framework by devising novel state representation and reward function and learns the travel recommendation and keywords generation simultaneously. To the best of our knowledge, this is the first time that keywords are explicitly discussed and used in RL-based travel recommendations. Extensive experiments are performed on the real-world datasets and the results clearly show the superior performance of KDRL compared with the baseline methods.

2 citations

Journal ArticleDOI
TL;DR: In this article , a drift-aware incremental learning framework based on ensemble learning is proposed to address catastrophic forgetting in CTR prediction in industrial data streams, where the model simply adapts to new data distribution all the time.
Abstract: Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over time and may recur. This can lead to catastrophic forgetting if the model simply adapts to new data distribution all the time. Also, it's inefficient to relearn distribution that has been occurred. Due to memory constraints and diversity of data distributions in large-scale industrial applications, conventional strategies for catastrophic forgetting such as replay, parameter isolation, and knowledge distillation are difficult to be deployed. In this work, we design a novel drift-aware incremental learning framework based on ensemble learning to address catastrophic forgetting in CTR prediction. With explicit error-based drift detection on streaming data, the framework further strengthens well-adapted ensembles and freezes ensembles that do not match the input distribution avoiding catastrophic interference. Both evaluations on offline experiments and A/B test shows that our method outperforms all baselines considered.

1 citations