OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering
Authors: Chenglong Ma, Yongli Ren, Pablo Castells, Mark Sanderson
Venue: SIGIR
Year: 2022

Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms.
A Python Interface to PISA!
Authors: Sean MacAvaney, Craig Macdonald
Venue: SIGIR
Year: 2022

PISA (Performant Indexes and Search for Academia) provides very efficient implementations of various retrieval algorithms over sparse inverted indices. The highly-optimized C++ implementation, however, has previously only been accessible via command line tools. From indexing to retrieval, 5--6 commands need to be executed in sequence, making the process relatively involved. Further complications when using PISA include a lengthy build process and minimal interoperability with other tools. In this work, we demonstrate a new tool that provides a native Python wrapper around PISA. The wrapper features a simplified interface that adheres to the PyTerrier API, making it easy to use (e.g., via Pandas DataFrames), apply to a multitude of datasets (e.g., those from the ir_datasets package) and combine with other methods (e.g., neural re-ranking and dense retrieval methods).
Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval
Authors: Kelong Mao, Zhicheng Dou, Hongjin Qian
Venue: SIGIR
Year: 2022

Conversational search is a crucial and promising branch in information retrieval. In this paper, we reveal that not all historical conversational turns are necessary for understanding the intent of the current query. The redundant noisy turns in the context largely hinder the improvement of search performance. However, enhancing the context denoising ability for conversational search is quite challenging due to data scarcity and the steep difficulty for simultaneously learning conversational query encoding and context denoising. To address these issues, in this paper, we present a novel Curriculum cOntrastive conTExt Denoising framework, COTED, towards few-shot conversational dense retrieval. Under a curriculum training order, we progressively endow the model with the capability of context denoising via contrastive learning between noised samples and denoised samples generated by a new conversation data augmentation strategy. Three curriculums tailored to conversational search are exploited in our framework. Extensive experiments on two few-shot conversational search datasets, i.e., CAsT-19 and CAsT-20, validate the effectiveness and superiority of our method compared with the state-of-the-art baselines.
MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization
Authors: Qianren Mao, Hongdong Zhu, Junnan Liu, Cheng Ji, Hao Peng, Jianxin Li, Lihong Wang, Zheng Wang
Venue: SIGIR
Year: 2022

Recent studies of extractive text summarization have leveraged BERT for document encoding with breakthrough performance. However, when using a pre-trained BERT-based encoder, existing approaches for selecting representative sentences for text summarization are inadequate since the encoder is not explicitly trained for representing sentences. Simply providing the BERT-initialized sentences to cross-sentential graph-based neural networks (GNNs) to encode semantic features of the sentences is not ideal because doing so fail to integrate other summary-worthy features like sentence importance and positions. This paper presents MuchSUM, a better approach for extractive text summarization. MuchSUM is a multi-channel graph convolutional network designed to explicitly incorporate multiple salient summary-worthy features. Specifically, we introduce three specific graph channels to encode the node textual features, node centrality features, and node position features, respectively, under bipartite word-sentence heterogeneous graphs. Then, a cross-channel convolution operation is designed to distill the common graph representations shared by different channels. Finally, the sentence representations of each channel are fused for extractive summarization. We also investigate three weighted graphs in each channel to infuse edge features for graph-based summarization modeling. Experimental results demonstrate our model can achieve considerable performance compared with some BERT-initialized graph-based extractive summarization systems.
From Cluster Ranking to Document Ranking
Authors: Egor Markovskiy, Fiana Raiber, Shoham Sabach, Oren Kurland
Venue: SIGIR
Year: 2022

The common approach of using clusters of similar documents for ad hoc document retrieval is to rank the clusters in response to the query; then, the cluster ranking is transformed to document ranking. We present a novel supervised approach to transform cluster ranking to document ranking. The approach allows to simultaneously utilize different clusterings and the resultant cluster rankings; this helps to improve the modeling of the document similarity space. Empirical evaluation shows that using our approach results in performance that substantially transcends the state-of-the-art in cluster-based document retrieval.
Regulating Group Exposure for Item Providers in Recommendation
Authors: Mirko Marras, Ludovico Boratto, Guilherme Ramos, Gianni Fenu
Venue: SIGIR
Year: 2022

Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certain provider groups have low representation of items in the catalog and, thus, in the user interactions. Then, we envision a scenario wherein platform owners seek to control the degree of exposure to such groups in the recommendation process. To support this scenario, we rely on disparate exposure measures that characterize the gap between the share of recommendations given to groups and the target level of exposure pursued by the platform owners. We then propose a re-ranking procedure that ensures desired levels of exposure are met. Experiments show that, while supporting certain groups of providers by rendering them with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility.
Continual Learning Dialogue Systems - Learning during Conversation
Authors: Sahisnu Mazumder, Bing Liu
Venue: SIGIR
Year: 2022

Dialogue systems, commonly known as Chatbots, have gained escalating popularity in recent years due to their wide-spread applications in carrying out chit-chat conversations with users and accomplishing various tasks as personal assistants. However, they still have some major weaknesses. One key weakness is that they are typically trained from pre-collected and manually-labeled data and/or written with handcrafted rules. Their knowledge bases (KBs) are also fixed and pre-compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, when these systems are deployed, the level of user satisfactory is often low. In this tutorial, we introduce and discuss methods to give chatbots the ability to continuously and interactively learn new knowledge during conversation, i.e. "on-the-job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and improve their performance over time. The first half of the tutorial focuses on introducing the paradigm of lifelong and continual learning and discuss various related problems and challenges in conversational AI applications. In the second half, we present recent advancements on the topic, with a focus on continuous lexical and factual knowledge learning in dialogues, open-domain dialogue learning after deployment and learning of new language expressions via user interactions for language grounding applications (e.g. natural language interfaces). Finally, we conclude with a discussion on the scopes for continual conversational skill learning and present some open challenges for future research.
Information Need Awareness: An EEG Study
Authors: Dominika Michalkova, Mario Parra-Rodriguez, Yashar Moshfeghi
Venue: SIGIR
Year: 2022

A fundamental goal of Information Retrieval (IR) is to satisfy searchers' information need (IN). Advances in neuroimaging technologies have allowed for interdisciplinary research to investigate the brain activity associated with the realisation of IN. While these studies have been informative, they were not able to capture the cognitive processes underlying the realisation of IN and the interplay between them with a high temporal resolution. This paper aims to investigate this research question by inferring the variability of brain activity based on the contrast of a state of IN with the two other (no-IN) scenarios. To do so, we employed Electroencephalography (EEG) and constructed an Event-Related Potential (ERP) analysis of the brain signals captured while the participants were experiencing the realisation of IN. In particular, the brain signals of 24 healthy participants were captured while performing a Question-Answering (Q/A) Task. Our results show a link between the early stages of processing, corresponding to awareness and the late activity, meaning memory control mechanisms. Our findings also show that participants exhibited early N1-P2 complex indexing awareness processes and indicate, thus, that the realisation of IN is manifested in the brain before it reaches the user's consciousness. This research contributes novel insights into a better understanding of IN and informs the design of IR systems to better satisfy it.
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer
Authors: Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Da Luo, Kangyi Lin, Junzhou Huang, Sophia Ananiadou, Peilin Zhao
Venue: SIGIR
Year: 2022

Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. In short, Neighbor-Interaction based CTR prediction involves the local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to guide the representation learning of the local neighbourhood, we further consider different kinds of interactions among the local neighborhood nodes from both explicit and implicit perspective, and propose a novel Graph-Masked Transformer (GMT) to effectively incorporates these kinds of interactions to produce highly representative embeddings for the target user-item pair. Moreover, in order to improve model robustness against neighbour sampling, we enforce a consistency regularization loss over the neighbourhood embedding. We conduct extensive experiments on two real-world datasets with millions of instances and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. Meanwhile, the comprehensive ablation studies verify the effectiveness of every component of our model. Furthermore, we have deployed this framework on the WeChat Official Account Platform with billions of users. The online A/B tests demonstrate an average CTR improvement of 21.9% against all online baselines.
Users: Can't Work With Them, Can't Work Without Them?
Authors: Alistair Moffat
Venue: SIGIR
Year: 2022

If we could design the ideal IR "effectiveness" experiment (as distinct from an IR "efficiency" experiment), what would it look like? It would probably be a lab-based observational study [3] involving multiple search systems masked behind a uniform interface, and with hundreds (or thousands) of users each progressing some "real" search activity they were interested in. And we'd plan to (non-intrusively, somehow) capture per-snippet, per-document, per-SERP, and per-session annotations and satisfaction responses. The collected data could then be compared against a range of measured "task completion quality" indicators, and also against search effectiveness metric scores computed from the elements contained in the SERPs that were served by the systems. That's a tremendously big ask! So we often use offline evaluation techniques instead, employing test collections, static qrels sets, and effectiveness metrics [6]. We abstract the user into a deterministic evaluation script, supposing for pragmatic reasons that we know what query they would issue, and at the same time assuming that we can apply an effectiveness metric to calculate how much usefulness (or satisfaction) they will derive from any given SERP. The great advantage of this approach is that aside from the process of collecting the qrels, it is free of the need for users, meaning that it is repeatable. Indeed, we often do repeat, iterating to set parameters (and to rectify programming errors). Then, once metric scores have been computed, we carry out one or more paired statistical tests and draw conclusions as to relative system effectiveness.
A Flexible Framework for Offline Effectiveness Metrics
Authors: Alistair Moffat, Joel Mackenzie, Paul Thomas, Leif Azzopardi
Venue: SIGIR
Year: 2022

The use of offline effectiveness metrics is one of the cornerstones of evaluation in information retrieval. Static resources that include test collections and sets of topics, the corresponding relevance judgments connecting them, and metrics that map document rankings from a retrieval system to numeric scores have been used for multiple decades as an important way of comparing systems. The basis behind this experimental structure is that the metric score for a system can serve as a surrogate measurement for user satisfaction. Here we introduce a user behavior framework that extends the C/W/L family. The essence of the new framework - which we call C/W/L/A - is that the user actions that are undertaken while reading the ranking can be considered separately from the benefit that each user will have derived as they exit the ranking. This split structure allows the great majority of current effectiveness metrics to be systematically categorized, and thus their relative properties and relationships to be better understood; and at the same time permits a wide range of novel combinations to be considered. We then carry out experiments using relevance judgments, document rankings, and user satisfaction data from two distinct sources, comparing the patterns of metric scores generated, and showing that those metrics vary quite markedly in terms of their ability to predict user satisfaction.
IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction
Authors: Ambreen Nazir, Yuan Rao
Venue: SIGIR
Year: 2022

Recently, the aspect-opinion term pairs (AOTP) extraction task has gained substantial importance in the domain of aspect-based sentiment analysis. It intends to extract the potential pair of each aspect term with its corresponding opinion term present in a user review. Some existing studies heavily relied on the annotated aspect terms and/or opinion terms, or adopted external knowledge/resources to figure out the task. Therefore, in this study, we propose a novel end-to-end solution, called an Interactive AOTP (IAOTP) model, for exploring AOTP. The IAOTP model first tracks the boundary of each token in given aspect-specific and opinion-specific representations through a span-based operation. Next, it generates the candidate AOTP by formulating the dyadic relations between tokens through the Biaffine transformation. Then, it computes the positioning information to capture the significant distance relationship that each candidate pair holds. And finally, it jointly models collaborative interactions and prediction of AOTP through a 2D self-attention. Besides the IAOTP model, this study also proposes an independent aspect/opinion encoding model (a RS model) that formulates relational semantics to obtain aspect-specific and opinion-specific representations that can effectively perform the extraction of aspect and opinion terms. Detailed experiments conducted on the publicly available benchmark datasets for AOTP, aspect terms, and opinion terms extraction tasks, clearly demonstrate the significantly improved performance of our models relative to other competitive state-of-the-art baselines.
SoChainDB: A Database for Storing and Retrieving Blockchain-Powered Social Network Data
Authors: Hoang H. Nguyen, Dmytro Bozhkov, Zahra Ahmadi, Nhat-Minh Nguyen, Thanh-Nam Doan
Venue: SIGIR
Year: 2022

Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Authors: Dan Saattrup Nielsen, Ryan McConville
Venue: SIGIR
Year: 2022

Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. Training these machine learning models require datasets of sufficient scale, diversity and quality. However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformation graph dataset (MuMiN), containing rich social media data (tweets, replies, users, images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade. The dataset is made available as a heterogeneous graph via a Python package (mumin). We provide baseline results for two node classification tasks related to the veracity of a claim involving social media, and demonstrate that these are challenging tasks, with the highest macro-average F1-score being 62.55% and 61.45% for the two tasks, respectively. The MuMiN ecosystem is available at https://mumin-dataset.github.io/, including the data, documentation, tutorials and leaderboards.
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity
Authors: Harrie Oosterhuis
Venue: SIGIR
Year: 2022

Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computational complexity of existing methods does not scale well with the length of the rankings, i.e. the ranking cutoff, nor with the item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm that performs unbiased gradient estimation with a computational complexity comparable to the best sorting algorithms. As a result, our novel learning-to-rank method is applicable in any scenario where standard sorting is feasible in reasonable time. Our experimental results indicate large gains in the time required for optimization, without any loss in performance. For the field, our contribution could potentially allow state-of-the-art learning-to-rank methods to be applied to much larger scales than previously feasible.
The 10th International Workshop on News Recommendation and Analytics (INRA 2022)
Authors: Özlem Özgöbek, Andreas Lommatzsch, Benjamin Kille, Peng Liu, Jon Atle Gulla, Edward C. Malthouse
Venue: SIGIR
Year: 2022

A rapidly changing news ecosystem presents new challenges to research, media organizations, consumers, and societies. The 10th edition of the International Workshop on News Recommendation and Analytics (INRA) serves to exchange ideas and discuss recent trends, technological advancements, and open problems concerning news. We welcome contributions in scientific articles, demonstrations, and ideas. We strive to bring together researchers, practitioners, and decision-makers to address crucial challenges. The workshop provides an opportunity to learn about recent research and interactively discuss technical and interdisciplinary aspects related to news. Topics of interest include information access systems for news, advances in natural language processing, multi-modality, mis- and disinformation, trust and user experiences, and personalization.