OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Channel Sellers Traffic Allocation in Large-scale E-commerce Promotion
Authors: Shen Xin, Yizhou Ye, Martin Ester, Cheng Long, Jie Zhang, Zhao Li, Kaiying Yuan, Yanghua Li
Venue: CIKM
Year: 2020

Large-scale online promotions, such as Double 11 and Black Friday, are of great value to e-commerce platforms nowadays. Traditional methods are not successful when we aim to maximize global Gross Merchandise Volume (GMV) in the promotion scenarios due to three limitations. The first is that the GMV of sellers varies significantly from daily scenarios to promotions. Second, these methods do not consider explosive demands in promotions, so that a consumer may fail to purchase some popular items due to sellers' limited capacities. Third, the traffic distribution over sellers presents divergence in different channels, thus rendering the performance of the traditional single-channel methods far from optimal in creating commercial values. To address these problems, we design a Multi-Channel Sellers Traffic Allocation (MCSTA) optimization model to obtain optimal page view (PV) distribution concerning global GMV. Then we propose a general constrained non-smooth convex optimization solution with a Multi-Objective Shortest Distance (MOSD) hyperparameter tuning method to solve MCSTA. This is the first work to systematically address this issue in the scenario of large-scale online promotions. The empirical results show that MCSTA achieves significant improvement of GMV by 1.1% based on A/B test during Alibaba's "Global Shopping Festival", one of the world's largest online sales events. Furthermore, we deploy MCSTA in other popular scenarios, including everyday promotion and video live stream service, to showcase that MCSTA can be widely applied in e-commerce and online entertainment services.
Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
Authors: Silei Xu, Giovanni Campagna, Jian Li, Monica S. Lam
Venue: CIKM
Year: 2020

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few annotations for each field. The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model. We use Schema2QA to generate Q&A systems for five Schema.org domains, restaurants, people, movies, books and music, and obtain an overall accuracy between 64% and 75% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a Schema.org schema, no additional manual effort is needed to create a Q&A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, obtaining a 64% accuracy on crowdsourced questions with no manual effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions that can be answered using Schema.org. Its performance is comparable to Google Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all these assistants by at least 18% on more complex, long-tail questions.
Controllable Multi-Character Psychology-Oriented Story Generation
Authors: Feifei Xu, Xinpeng Wang, Yunpu Ma, Volker Tresp, Yuyi Wang, Shanlin Zhou, Haizhou Du
Venue: CIKM
Year: 2020

Story generation, which aims to generate a long and coherent story automatically based on the title or an input sentence, is an important research area in the field of natural language generation. There is relatively little work on story generation with appointed emotions. Most existing works focus on using only one specific emotion to control the generation of a whole story and ignore the emotional changes in the characters in the course of the story. In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters. To the best of our knowledge, this work is first to focuses on characters' emotional lines in story generation. We present a novel model-based attention mechanism that we call SoCP (Storytelling of multi-Character Psychology). We show that the proposed model can generate stories considering the changes in the psychological state of different characters. To take into account the particularity of the model, in addition to commonly used evaluation indicators(BLEU, ROUGE, etc.), we introduce the accuracy rate of psychological state control as a novel evaluation metric. The new indicator reflects the effect of the model on the psychological state control of story characters. Experiments show that with SoCP, the generated stories follow the psychological state for each character according to both automatic and human evaluations.
Learning to Generate Reformulation Actions for Scalable Conversational Query Understanding
Authors: Zihan Xu, Jiangang Zhu, Ling Geng, Yang Yang, Bojia Lin, Daxin Jiang
Venue: CIKM
Year: 2020

The ability of conversational query understanding (CQU) is indispensable to multi-turn QA. However, existing methods are data-driven and expensive to extend to new conversation domains, or under specific frameworks and hard to apply to other underlying QA technologies. We propose a novel contextual query reformulation (CQR) module based on reformulation actions for general CQU. The actions are domain-independent and scalable, since they capture syntactic regularities of conversations. For action generation, we propose a multi-task learning framework enhanced by coreference resolution, and introduce grammar constraints into the decoding process. Then CQR synthesizes standalone queries based on the actions, which naturally adapts to original downstream technologies. Experiments on different CQU datasets suggest that action-based methods substantially outperform direct reformulation, and the proposed model performs the best among the methods.
GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems
Authors: Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates
Venue: CIKM
Year: 2020

Given the convenience of collecting information through online services, recommender systems now consume large scale data and play a more important role in improving user experience. With the recent emergence of Graph Neural Networks (GNNs), GNN-based recommender models have shown the advantage of modeling the recommender system as a user-item bipartite graph to learn representations of users and items. However, such models are expensive to train and difficult to perform frequent updates to provide the most up-to-date recommendations. In this work, we propose to update GNN-based recommender models incrementally so that the computation time can be greatly reduced and models can be updated more frequently. We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion. Our approach preserves a user's long-term preference (or an item's long-term property) during incremental model updating. GraphSAIL implements a graph structure preservation strategy which explicitly preserves each node's local structure, global structure, and self-information, respectively. We argue that our incremental training framework is the first attempt tailored for GNN based recommender systems and demonstrate its improvement compared to other incremental learning techniques on two public datasets. We further verify the effectiveness of our framework on a large-scale industrial dataset.
Ranking User Attributes for Fast Candidate Selection in Recommendation Systems
Authors: Huichao Xue
Venue: CIKM
Year: 2020

Many recommendation systems use users' attributes to retrieve documents before ranking. Instead of using all attributes, this work explores algorithms that choose a subset, in order to achieve higher precision. We propose a model that forecasts the relevance of documents matched by each individual attribute. By restricting to top-K attributes based on the forecast, we observed 50% reduction in latency at 99th percentile on LinkedIn's job recommendation system, as well as increased users' engagements.
Learning to Build User-tag Profile in Recommendation System
Authors: Su Yan, Xin Chen, Ran Huo, Xu Zhang, Leyu Lin
Venue: CIKM
Year: 2020

User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e.g. gender, age, and location) and user behavior information (e.g. browsing and search history). Among different dimensions of user profiling, tagging is an explainable and widely-used representation of user interest. In this paper, we propose a user tag profiling model (UTPM) to study user-tag profiling as a multi-label classification task using deep neural networks. Different from the conventional model, our UTPM model is a multi-head attention mechanism with shared query vectors to learn sparse features across different fields. Besides, we introduce the improved FM-based cross feature layer, which outperforms many state-of-the-art cross feature methods and further enhances model performance. Meanwhile, we design a novel joint method to learn the preference of different tags from a single clicked news article in recommendation systems. Furthermore, our UTPM model is deployed in the WeChat "Top Stories" recommender system, where both online and offline experiments demonstrate the superiority of the proposed model over baseline models.
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching
Authors: Liu Yang, Mingyang Zhang, Cheng Li, Michael Bendersky, Marc Najork
Venue: CIKM
Year: 2020

Many natural language processing and information retrieval problems can be formalized as the task of semantic matching. Existing work in this area has been largely focused on matching between short texts (e.g., question answering), or between a short and a long text (e.g., ad-hoc retrieval). Semantic matching between long-form documents, which has many important applications like news recommendation, related article recommendation and document clustering, is relatively less explored and needs more research effort. In recent years, self-attention based models like Transformers and BERT have achieved state-of-the-art performance in the task of text matching. These models, however, are still limited to short text like a few sentences or one paragraph due to the quadratic computational complexity of self-attention with respect to input text length. In this paper, we address the issue by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input. We propose a transformer based hierarchical encoder to capture the document structure information. In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT. Our experimental results on several benchmark data sets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art models including hierarchical attention, multi-depth attention-based hierarchical recurrent neural network, and BERT. Comparing to BERT based baselines, our model is able to increase maximum input text length from 512 to 2048. We will open source a Wikipedia based benchmark data set, code and a pre-trained model to accelerate future research on long-form document matching.
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Authors: Tao Yang, Shikai Fang, Shibo Li, Yulan Wang, Qingyao Ai
Venue: CIKM
Year: 2020

Leveraging biased click data for optimizing learning to rank systems has been a popular approach in information retrieval. Because click data is often noisy and biased, a variety of methods have been proposed to construct unbiased learning to rank (ULTR) algorithms for the learning of unbiased ranking models. Among them, automatic unbiased learning to rank (AutoULTR) algorithms that jointly learn user bias models (i.e., propensity models) with unbiased rankers have received a lot of attention due to their superior performance and low deployment cost in practice. Despite their differences in theories and algorithm design, existing studies on ULTR usually use uni-variate ranking functions to score each document or result independently. On the other hand, recent advances in context-aware learning-to-rank models have shown that multivariate scoring functions, which read multiple documents together and predict their ranking scores jointly, are more powerful than uni-variate ranking functions in ranking tasks with human-annotated relevance labels. Whether such superior performance would hold in ULTR with noisy data, however, is mostly unknown. In this paper, we investigate existing multivariate scoring functions and AutoULTR algorithms in theory and prove that permutation invariance is a crucial factor that determines whether a context-aware learning-to-rank model could be applied to existing AutoULTR framework. Our experiments with synthetic clicks on two large-scale benchmark datasets show that AutoULTR models with permutation-invariant multivariate scoring functions significantly outperform those with uni-variate scoring functions and permutation-variant multivariate scoring functions.
Query-aware Tip Generation for Vertical Search
Authors: Yang Yang, Junmei Hao, Canjia Li, Zili Wang, Jingang Wang, Fuzheng Zhang, Rao Fu, Peixu Hou, Gong Zhang, Zhongyuan Wang
Venue: CIKM
Year: 2020

As a concise form of user reviews, tips have unique advantages to explain the search results, assist users' decision making, and further improve user experience in vertical search scenarios. Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios. To address this issue, this paper proposes a query-aware tip generation framework, integrating query information into encoding and subsequent decoding processes. Two specific adaptations of Transformer and Recurrent Neural Network (RNN) are proposed. For Transformer, the query impact is incorporated into the self-attention computation of both the encoder and the decoder. As for RNN, the query-aware encoder adopts a selective network to distill query-relevant information from the review, while the query-aware decoder integrates the query information into the attention computation during decoding. The framework consistently outperforms the competing methods on both public and real-world industrial datasets. Last but not least, online deployment experiments on Dianping demonstrate the advantage of the proposed framework for tip generation as well as its online business values.
Semi-supervised Consensus Clustering Based on Frequent Closed Itemsets
Authors: Tianshu Yang, Nicolas Pasquier, Antoine Hom, Laurent Dollé, Frédéric Precioso
Venue: CIKM
Year: 2020

Semi-supervised consensus clustering integrates supervised information into consensus clustering in order to improve the quality of clustering. In this paper, we study the novel Semi-MultiCons semi-supervised consensus clustering method extending the previous MultiCons approach. Semi-MultiCons aims to improve the clustering result by integrating pairwise constraints in the consensus creation process and infer the number of clusters K using frequent closed itemsets extracted from the ensemble members. Experimental results show that the proposed method outperforms other state-of-art semi-supervised consensus algorithms.
Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification
Authors: Kaijia Yang, Nantao Zheng, Xinyu Dai, Liang He, Shujian Huang, Jiajun Chen
Venue: CIKM
Year: 2020

Recently few-shot relation classification has drawn much attention. It devotes to addressing the long-tail relation problem by recognizing the relations from few instances. The existing metric learning methods aim to learn the prototype of classes and make prediction according to distances between query and prototypes. However, it is likely to make unreliable predictions due to the text diversity. It is intuitive that the text descriptions of relation and entity can provide auxiliary support evidence for relation classification. In this paper, we propose TD-Proto, which enhances prototypical network with relation and entity descriptions. We design a collaborative attention module to extract beneficial and instructional information of sentence and entity respectively. A gate mechanism is proposed to fuse both information dynamically so as to obtain a knowledge-aware instance. Experimental results demonstrate that our method achieves excellent performance.
LB-CGM: Latent Based Conditional Generative Model with Reliable Distribution Prediction
Authors: Yichen Yao, Guozheng Li, Yujie Chen, Rongqi Li, Yinzhi Zhou, Xiaodong Zhang, Haoyuan Hu, Yinghui Xu
Venue: CIKM
Year: 2020

Randomness exists either due to the inherent noise of the problem or lack of important input features, which could lead to multimodality of the data distribution. Therefore, in more and more scenarios, it is required not only to predict a single point-value, but also the distribution of the prediction. However, well-studied prediction models usually focus on point prediction that minimizes the mean squared error or the mean absolute error. These approaches could miss important knowledge when their outputs are applied to the downstream decision process. In this paper, we combine the advantages of both GANs (Generative Adversarial Nets) and VAEs (Variational Auto-Encoders), and introduce a latent-based conditional generative model (LB-CGM) to handle the distribution regression problems. The VAE framework is adopted, and the adversarial network is applied to estimate the validity of the generated sample. Besides, the latent-based reconstruction loss is introduced to mitigate mode collapse, in which the direct pairwise comparison between the original and generated samples ensures the correctness and completeness of the generated mode pattern. In this work, we explore a path for the generative model to be used in probabilistic prediction problems. This method can produce conditional prediction distribution close to the actual distribution and is verified on both the synthetic dataset and benchmark dataset.
BotSpot: A Hybrid Learning Framework to Uncover Bot Install Fraud in Mobile Advertising
Authors: Tianjun Yao, Qing Li, Shangsong Liang, Yadong Zhu
Venue: CIKM
Year: 2020

Mobile advertising has become inarguably one of the fastest growing industries all over the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. There are many tricks a fraudster can leverage, among which bot install fraud is undoubtedly the most insidious one due to its ability to implement sophisticated behavioral patterns and emulate normal users, so as to evade from detection rules defined by human experts. In this work, we propose an anti-fraud method based on heterogeneous graph that incorporates both local context and global context via graph neural networks (GNN) and gradient boosting classifier to detect bot fraud installs at Mobvista, a leading global mobile advertising company. Offline evaluations in two datasets show the proposed method outperforms all the competitive baseline methods by at least 2.2% in the first dataset and 5.75% in the second dataset given the evaluation metric Recall@90% Precision. Furthermore, we deploy our method to tackle million-scale data daily at Mobvista. The online performance also shows that the proposed methods consistently detect more bots than other baseline methods.
Time-aware Graph Relational Attention Network for Stock Recommendation
Authors: Xiaoting Ying, Cong Xu, Jianliang Gao, Jianxin Wang, Zhao Li
Venue: CIKM
Year: 2020

Recommending stock with the highest return ratio is always a challenging problem in the field of financial technology. In this paper, we propose a time-aware graph relational attention network (TRAN) for stock recommendation based on return ratio ranking. In TRAN, time-aware relational attention mechanism is the key unit to capture time-varying correlation strength between stocks by the interaction of historical sequences and stock description documents. With the dynamic strength, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, our model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.
Who is Delivering My Food?: Detecting Food Delivery Abusers using Variational Reward Inference Networks
Authors: Daeyoung Yoon, Simon S. Woo
Venue: CIKM
Year: 2020

The recent paramount success of the gig economy has introduced new business opportunities in different areas such as food delivery service. However, there are food delivery ride abusers who break the company rule by driving unauthorized vehicles that are not stated in the contract. These abusers are particularly problematic because they break the transportation regulations and unfairly take more orders. However, detecting these abusers are challenging because of lack of labeled datasets and these anomalous abusers do not frequently occur compared to normal riders. Furthermore, sequential patterns of abusing behaviors are not easy to model. In this work, we aim to detect food delivery abusers using unauthorized vehicles, by formulating this problem as a novelty detection over sequential data. We propose the Variational Reward Inference based Novelty Detector (VRIND) using sequential novelty detection using inverse reinforcement learning with variational inference, in which the reward function can learn the behavioral intention of decision-making experts. The reward function is represented by a neural network that is capable of approximating reward distributions by variational reward inference. Using a commercial food delivery trajectory dataset from our company, we demonstrate that our model significantly outperforms over the other baseline methods in identifying novelty (abusers) in sequential data, which can ensure regulatory compliance and provide the fair opportunity to more than 100 thousand delivery riders, serving more than 1.5 million daily transactions in our Baemin food delivery system.
OFFER: A Motif Dimensional Framework for Network Representation Learning
Authors: Shuo Yu, Feng Xia, Jin Xu, Zhikui Chen, Ivan Lee
Venue: CIKM
Year: 2020

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.