OpenRaiser/Intern-Atlas huggingface dataset:
A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users
Authors: Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, Jing Xiao 0006
Venue: SIGIR
Year: 2020

Internet is changing the world, adapting to the trend of internet sales will bring revenue to traditional insurance companies. Online insurance is still in its early stages of development, where cold start problem (prospective customer) is one of the greatest challenges. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods couldn't be applied to insurance domain directly due to the domain's specific properties. In this paper, we propose a novel framework called a Heterogeneous information network based Cross Domain Insurance Recommendation (HCDIR) system for cold start users. Specifically, we first try to learn more effective user and item latent features in both source and target domains. In source domain, we employ gated recurrent unit (GRU) to module users' dynamic interests. In target domain, given the complexity of insurance products and the data sparsity problem, we construct an insurance heterogeneous information network (IHIN) based on data from PingAn Jinguanjia, the IHIN connects users, agents, insurance products and insurance product properties together, giving us richer information. Then we employ three-level (relational, node, and semantic) attention aggregations to get user and insurance product representations. After obtaining latent features of overlapping users, a feature mapping between the two domains is learned by multi-layer perceptron (MLP). We apply HCDIR on Jinguanjia dataset, and show HCDIR significantly outperforms the state-of-the-art solutions.
MarkedBERT: Integrating Traditional IR Cues in Pre-trained Language Models for Passage Retrieval
Authors: Lila Boualili, José G. Moreno 0001, Mohand Boughanem
Venue: SIGIR
Year: 2020

The Information Retrieval (IR) community has witnessed a flourishing development of deep neural networks, however, only a few managed to beat strong baselines. Among them, models like DRMM and DUET were able to achieve better results thanks to the proper handling of exact match signals. Nowadays, the application of pre-trained language models to IR tasks has achieved impressive results exceeding all previous work. In this paper, we assume that established IR cues like exact term-matching, proven to be valuable for deep neural models, can be used to augment the direct supervision from labeled data for training these pre-trained models. To study the effectiveness of this assumption, we propose MarkedBERT a modified version of one of the most popular pre-trained models via language modeling tasks, BERT. MarkedBERT integrates exact match signals using a marking technique that locates and highlights Exact Matched query-document terms using marker tokens. Experiments on MS MARCO Passage Ranking task show that our rather simple approach is actually effective. We find that augmenting the input with marker tokens allows the model to focus on valuable text sequences for IR.
How to Measure the Reproducibility of System-oriented IR Experiments
Authors: Timo Breuer 0002, Nicola Ferro 0001, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff
Venue: SIGIR
Year: 2020

Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception. Besides the problem of moving the field towards more reproducible experimental practices and protocols, we also face a severe methodological issue: we do not have any means to assess when reproduced is reproduced. Moreover, we lack any reproducibility-oriented dataset, which would allow us to develop such methods.
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension
Authors: Jie Cai, Zhengzhou Zhu, Ping Nie, Qian Liu 0012
Venue: SIGIR
Year: 2020

Pre-trained models have brought significant improvements to many NLP tasks and have been extensively analyzed. But little is known about the effect of fine-tuning on specific tasks. Intuitively, people may agree that a pre-trained model already learns semantic representations of words (e.g. synonyms are closer to each other) and fine-tuning further improves its capabilities which require more complicated reasoning (e.g. coreference resolution, entity boundary detection, etc). However, how to verify these arguments analytically and quantitatively is a challenging task and there are few works focus on this topic. In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e.g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task. Specifically, we identify five phenomena in MRC. According to pairwise probing tasks, we compare the performance of each layer's hidden representation of pre-trained and fine-tuned BERT. The proposed pairwise probe alleviates the problem of distraction from inaccurate model training and makes a robust and quantitative comparison. Our experimental analysis leads to highly confident conclusions: (1) Fine-tuning has little effect on the fundamental and low-level information and general semantic tasks. (2) For specific abilities required for downstream tasks, fine-tuned BERT is better than pre-trained BERT and such gaps are obvious after the fifth layer.
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Authors: Yuanjiang Cao, Xiaocong Chen, Lina Yao 0001, Xianzhi Wang 0001, Wei Emma Zhang
Venue: SIGIR
Year: 2020

Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several crafting methods.
Domain-Adaptive Neural Automated Essay Scoring
Authors: Yue Cao 0006, Hanqi Jin, Xiaojun Wan 0001, Zhiwei Yu 0001
Venue: SIGIR
Year: 2020

Automated essay scoring (AES) is a promising, yet challenging task. Current state-of-the-art AES models ignore the domain difference and cannot effectively leverage data from different domains. In this paper, we propose a domain-adaptive framework to improve the domain adaptability of AES models. We design two domain-independent self-supervised tasks and jointly train them with the AES task simultaneously. The self-supervised tasks enable the model to capture the shared knowledge across different domains and act as the regularization to induce a shared feature space. We further propose to enhance the model's robustness to domain variation via a novel domain adversarial training technique. The main idea of the proposed domain adversarial training is to train the model with small well-designed perturbations to make the model robust to domain variation. We obtain the perturbation via a variation of the Fast Gradient Sign Method (FGSM). Our approach achieves new state-of-the-art performance in both in-domain and cross-domain experiments on the ASAP dataset. We also show that the proposed domain adaptation framework is architecture-free and can be successfully applied to different models.
Relevance Models for Multi-Contextual Appropriateness in Point-of-Interest Recommendation
Authors: Anirban Chakraborty 0002, Debasis Ganguly, Owen Conlan
Venue: SIGIR
Year: 2020

Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.
Bundle Recommendation with Graph Convolutional Networks
Authors: Jianxin Chang, Chen Gao 0001, Xiangnan He 0001, Depeng Jin, Yong Li 0008
Venue: SIGIR
Year: 2020

Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles. In this work, we propose a graph neural network model named BGCN (short forBundle Graph Convolutional Network ) for bundle recommendation. BGCN unifies user-item interaction, user-bundle interaction and bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item level semantics. Through training based on hard-negative sampler, the user's fine-grained preferences for similar bundles are further distinguished. Empirical results on two real-world datasets demonstrate the strong performance gains of BGCN, which outperforms the state-of-the-art baselines by 10.77% to 23.18%.
Studying Product Competition Using Representation Learning
Authors: Fanglin Chen 0002, Xiao Liu, Davide Proserpio, Isamar Troncoso, Feiyu Xiong
Venue: SIGIR
Year: 2020

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it is computationally challenging to analyze product-level competition for the millions of products available on e-commerce platforms. We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec, to study product-level competition, when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional embedding that preserves important product information. In order for the product embeddings to be useful for firm strategic decision making, we leverage economic theories and causal inference to propose two modifications to Word2Vec. First of all, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Second, we combine these vectors with random utility-based choice models to forecast demand. To accurately estimate price elasticities, i.e., how demand responds to changes in price, we modify Word2Vec by removing the influence of price from the product vectors. We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.
Metadata Matters in User Engagement Prediction
Authors: Xiang Chen 0010, Saayan Mitra, Viswanathan Swaminathan 0001
Venue: SIGIR
Year: 2020

Predicting user engagement (e.g., click-through rate, conversion rate) on the display ads plays a critical role in delivering the right ad to the right user in online advertising. Existing techniques spanning Logistic Regression to Factorization Machines and their derivatives, focus on modeling the interactions among handcrafted features to predict the user engagement. Little attention has been paid on how the ad fits with the context (e.g., hosted webpage, user demographics). In this paper, we propose to include the metadata feature, which captures the visual appearance of the ad, in the user engagement prediction task. In particular, given a data sample, we combine both the basic context features, which have been widely used in existing prediction models, and the metadata feature, which is extracted from the ad using a state-of-the-art deep learning framework, to predict user engagement. To demonstrate the effectiveness of the proposed metadata feature, we compare the performance of the widely used prediction models before and after integrating the metadata feature. Our experimental results on a real-world dataset demonstrate that the metadata feature is able to further improve the prediction performance.
AIIS: The SIGIR 2020 Workshop on Applied Interactive Information Systems
Authors: Hongshen Chen, Zhaochun Ren, Pengjie Ren, Dawei Yin 0001, Xiaodong He 0001
Venue: SIGIR
Year: 2020

Nowadays, intelligent information systems, especially the interactive information systems (e.g., conversational interaction systems like Siri, and Cortana; news feed recommender systems, and interactive search engines, etc.), are ubiquitous in real-world applications. These systems either converse with users explicitly through natural languages, or mine users interests and respond to users requests implicitly. Interactivity has become a crucial element towards intelligent information systems. Despite the fact that interactive information systems have gained significant progress, there are still many challenges to be addressed when applying these models to real-world scenarios. This half day workshop explores challenges and potential research, development, and application directions in applied interactive information systems. We aim to discuss the issues of applying interactive information models to production systems, as well as to shed some light on the fundamental characteristics, i.e., interactivity and applicability, of different interactive tasks. We welcome practical, theoretical, experimental, and methodological studies that advances the interactivity towards intelligent information systems. The workshop aims to bring together a diverse set of practitioners and researchers interested in investigating the interaction between human and information systems to develop more intelligent information systems.
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval
Authors: Limin Chen, Zhiwen Tang, Grace Hui Yang
Venue: SIGIR
Year: 2020

Interactive Information Retrieval (IIR) and Reinforcement Learning (RL) share many commonalities, including an agent who learns while interacts, a long-term and complex goal, and an algorithm that explores and adapts. To successfully apply RL methods to IIR, one challenge is to obtain sufficient relevance labels to train the RL agents, which are infamously known as sample inefficient. However, in a text corpus annotated for a given query, it is not the relevant documents but the irrelevant documents that predominate. This would cause very unbalanced training experiences for the agent and prevent it from learning any policy that is effective. Our paper addresses this issue by using domain randomization to synthesize more relevant documents for the training. Our experimental results on the Text REtrieval Conference (TREC) Dynamic Domain (DD) 2017 Track show that the proposed method is able to boost an RL agent's learning effectiveness by 22% in dealing with unseen situations.
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
Authors: Chong Chen 0001, Min Zhang 0006, Weizhi Ma, Yiqun Liu 0001, Shaoping Ma
Venue: SIGIR
Year: 2020

Knowledge graph (KG) contains well-structured external information and has shown to be effective for high-quality recommendation. However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG. While for model learning, these methods mainly rely on Negative Sampling (NS) to optimize the models for both KG embedding task and recommendation task. Since NS is not robust (e.g., sampling a small fraction of negative instances may lose lots of useful information), it is reasonable to argue that these methods are insufficient to capture collaborative information among users, items, and entities.
Attending to Inter-sentential Features in Neural Text Classification
Authors: Billy Chiu, Sunil Kumar Sahu, Neha Sengupta, Derek Thomas, Mohammady Mahdy
Venue: SIGIR
Year: 2020

Text classification requires a deep understanding of the linguistic features in text; in particular, the intra-sentential (local) and inter-sentential features (global). Models that operate on word sequences have been successfully used to capture the local features, yet they are not effective in capturing the global features in long-text. We investigate graph-level extensions to such models and propose a novel architecture for combining alternative text features. It uses an attention mechanism to dynamically decide how much information to use from a sequence- or graph-level component. We evaluated different architectures on a range of text classification datasets, and graph-level extensions were found to improve performance on most benchmarks. In addition, the attention-based architecture, as adaptively-learned from the data, outperforms the generic and fixed-value concatenation ones.
From Information to Assistance
Authors: Elizabeth F. Churchill
Venue: SIGIR
Year: 2020

When Johnson was writing this, said libraries were very exclusive, inaccessible to most. When I was growing up the library was a favorite place to find information with the help of expert assistants, trained librarians. Nowadays, while libraries are still one of my favorite institutions, we have powerful digital information search, retrieval, and assemblage services, bundled into easily accessible tools at our fingertips. As information proliferates and human information needs remain high, information retrieval will continue to be a central area of investigation. We will also need better and better tools to access, assemble, and represent that information in ways that can be understood and applied-tools that ensure information turns into knowledge that is useful and used. In this talk, I will focus on how people find information, and how the tools we build aid in that finding. Using case studies, I will outline some that remain challenging, and offer some case studies and edge cases where more work is needed. I will share thoughts on how emerging assistant devices and services are and are not meeting the challenge of becoming expert information assistant
Agent Dialogue: A Platform for Conversational Information Seeking Experimentation
Authors: Adam Czyzewski, Jeffrey Dalton 0001, Anton Leuski
Venue: SIGIR
Year: 2020

Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade state-of-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.
Retrieving Potential Causes from a Query Event
Authors: Suchana Datta, Debasis Ganguly, Dwaipayan Roy 0001, Francesca Bonin, Charles Jochim, Mandar Mitra
Venue: SIGIR
Year: 2020

Different to traditional IR, which retrieves a set of topically relevant documents given a user query, we investigate causal retrieval, which involves retrieving a set of documents that describe a set of potential causes leading to an effect specified in the query. We argue that the nature of causal relevance should be different to that of traditional topical relevance. This is because although the causally relevant documents would have partial term overlap with the ones that are topically relevant for a query, yet it is expected that a majority of these documents would use a different set of terms to describe a number of causes possibly leading to their effects. To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high weights in the topically relevant distribution, leading to potential causal relevance. Our experiments demonstrate that such a feedback model turns out to be substantially more effective than traditional IR models and a number of other causality heuristic baselines.
Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization
Authors: Yang Deng 0002, Wenxuan Zhang 0001, Yaliang Li, Min Yang 0007, Wai Lam, Ying Shen 0001
Venue: SIGIR
Year: 2020

Non-factoid question answering (QA) is one of the most extensive yet challenging application and research areas of retrieval-based question answering. In particular, answers to non-factoid questions can often be too lengthy and redundant to comprehend, which leads to the great demand on answer sumamrization in non-factoid QA. However, the multi-level interactions between QA pairs and the interrelation among different answer sentences are usually modeled separately on current answer summarization studies. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. Specifically, we design a hierarchical compare-aggregate method to integrate the interaction between QA pairs in both word-level and sentence-level into the final question and answer representations. After that, we conduct the question-aware sequential extractor to produce a summary for the lengthy answer. Experimental results show that answer summarization benefits from both hierarchical and sequential context modeling and our method achieves superior performance on WikiHowQA and PubMedQA.
Spatio-temporal Conditioned Language Models
Authors: Juglar Diaz
Venue: SIGIR
Year: 2020

The ubiquitous availability of mobile devices with GPS capabilities and the popularity of social media platforms have created a rich source for textual data with spatio-temporal information. Also, other domains like crime incident description and search engine queries, can provide spatio-temporal textual data. These data sources can be used to discover space-time related insights of human behavior. This work focuses on modeling text that is associated with a particular time and place. We extend the traditional language modeling task from natural language processing to language modeling under spatio-temporal conditions. This task definition allows us to use the same evaluation framework used in language modeling.