OpenRaiser/Intern-Atlas huggingface dataset:
Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation
Authors: Ke Tu, Peng Cui, Daixin Wang, Zhiqiang Zhang, Jun Zhou, Yuan Qi, Wenwu Zhu
Venue: Conference CIKM 2021
Year: 2021

Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relationships are not helpful for a target user-item prediction. To exploit the knowledge graph to capture target-specific knowledge relationships in recommender systems, we need to distill the knowledge graph to reserve the useful information and refine the knowledge to capture the users' preferences. To address the issues, we propose Knowledge-aware Conditional Attention Networks (KCAN), which is an end-to-end model to incorporate knowledge graph into a recommender system. Specifically, we use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then given a target, i.e., a user-item pair, we automatically distill the knowledge graph into the target-specific subgraph based on the knowledge-aware attention. Afterward, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations. Therefore, we can gain both representability and personalization to achieve overall performance. Experimental results on real-world datasets demonstrate the effectiveness of our framework over the state-of-the-art algorithms.
Fairness in recommendation ranking through pairwise comparisons
Authors: Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow
Venue: Conference KDD 2019
Year: 2019

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks, how can we quantify them, and how should we address them? In this paper we offer a set of novel metrics for evaluating algorithmic fairness concerns in recommender systems. In particular we show how measuring fairness based on pairwise comparisons from randomized experiments provides a tractable means to reason about fairness in rankings from recommender systems. Building on this metric, we offer a new regularizer to encourage improving this metric during model training and thus improve fairness in the resulting rankings. We apply this pairwise regularization to a large-scale, production recommender system and show that we are able to significantly improve the system's pairwise fairness.
Addressing bias and fairness in search systems
Authors: Ruoyuan Gao, Chirag Shah 0001
Venue: Conference SIGIR 2021
Year: 2021

Search systems have unprecedented influence on how and what information people access. These gateways to information on the one hand create an easy and universal access to online information, and on the other hand create biases that have shown to cause knowledge disparity and ill-decisions for information seekers. Most of the algorithms for indexing, retrieval, and ranking are heavily driven by the underlying data that itself is biased. In addition, orderings of the search results create position bias and exposure bias due to their considerable focus on relevance and user satisfaction. These and other forms of biases that are implicitly and sometimes explicitly woven in search systems are becoming increasing threats to information seeking and sense-making processes. In this tutorial, we will introduce the issues of biases in data, in algorithms, and overall in search processes and show how we could think about and create systems that are fairer, with increasing diversity and transparency. Specifically, the tutorial will present several fundamental concepts such as relevance, novelty, diversity, bias, and fairness using socio-technical terminologies taken from various communities, and dive deeper into metrics and frameworks that allow us to understand, extract, and materialize them. The tutorial will cover some of the most recent works in this area and show how this interdisciplinary research has opened up new challenges and opportunities for communities such as SIGIR.
Fare: Diagnostics for fair ranking using pairwise error metrics
Authors: Caitlin Kuhlman, MaryAnn Van Valkenburg, Elke A. Rundensteiner
Venue: Conference WWW 2019
Year: 2019

Ranking, used extensively online and as a critical tool for decision making across many domains, may embed unfair bias. Tools to measure and correct for discriminatory bias are required to ensure that ranking models do not perpetuate unfair practices. Recently, a number of error-based criteria have been proposed to assess fairness with regard to the treatment of protected groups (as determined by sensitive data attributes, e.g., race, gender, or age). However this has largely been limited to classification tasks, and error metrics used in these approaches are not applicable for ranking. Therefore, in this work we propose to broaden the scope of fairness assessment to include error-based fairness criteria for rankings. Our approach supports three criteria: Rank Equality, Rank Calibration, and Rank Parity, which cover a broad spectrum of fairness considerations from proportional group representation to error rate similarity. The underlying error metrics are formulated to be rank-appropriate, using pairwise discordance to measure prediction error in a model-agnostic fashion. Based on this foundation, we then design a fair auditing mechanism which captures group treatment throughout the entire ranking, generating in-depth yet nuanced diagnostics. We demonstrate the efficacy of our error metrics using real-world scenarios, exposing trade-offs among fairness criteria and providing guidance in the selection of fair-ranking algorithms.
Quantifying the Impact of User Attentionon Fair Group Representation in Ranked Lists
Authors: Piotr Sapiezynski, Wesley Zeng, Ronald E. Robertson, Alan Mislove, Christo Wilson
Venue: Conference WWW 2019
Year: 2019

In this work, we introduce a novel metric for auditing group fairness in ranked lists. Our approach offers two benefits compared to the state of the art. First, we offer a blueprint for modeling of user attention. Rather than assuming a logarithmic loss in importance as a function of the rank, we can account for varying user behaviors through parametrization. For example, we expect a user to see more items during a viewing of a social media feed than when they inspect the results list of a single web search query. Second, we allow non-binary protected attributes to enable investigating inherently continuous attributes (e.g., political alignment on the liberal to conservative spectrum) as well as to facilitate measurements across aggregated sets of search results, rather than separately for each result list. By combining these two elements into our metric, we are able to better address the human factors inherent in this problem. We measure the whole sociotechnical system, consisting of a ranking algorithm and individuals using it, instead of exclusively focusing on the ranking algorithm. Finally, we use our metric to perform three simulated fairness audits. We show that determining fairness of a ranked output necessitates knowledge (or a model) of the end-users of the particular service. Depending on their attention distribution function, a fixed ranking of results can appear biased both in favor and against a protected group1.
Personal Entity, Concept, and Named Entity Linking in Conversations
Authors: Hideaki Joko, Faegheh Hasibi
Venue: Conference CIKM 2022
Year: 2022

Building conversational agents that can have natural and knowledge-grounded interactions with humans requires understanding user utterances. Entity Linking (EL) is an effective technique for understanding natural language text and connecting it to external knowledge. It is, however, shown that the existing EL methods developed for annotating documents are suboptimal for conversations, where concepts and personal entities (e.g., "my cars'') are essential for understanding user utterances. In this paper, we introduce a collection and a tool for entity linking in conversations. We provide EL annotations for 1,327 conversational utterances, consisting of links to named entities, concepts, and personal entities. The dataset is used for training our toolkit for conversational entity linking, CREL. Unlike existing EL methods, CREL is developed to identify both named entities and concepts. It also utilizes coreference resolution techniques to identify personal entities and their references to the explicit entity mentions in the conversations. We compare CREL with state-of-the-art techniques and show that it outperforms all existing baselines.
TaskMAD: A Platform for Multimodal Task-Centric Knowledge-Grounded Conversational Experimentation
Authors: Alessandro Speggiorin, Jeffrey Dalton, Anton Leuski
Venue: Conference SIGIR 2022
Year: 2022

The role of conversational assistants continues to evolve, beyond simple voice commands to ones that support rich and complex tasks in the home, car, and even virtual reality. Going beyond simple voice command and control requires agents and datasets blending structured dialogue, information seeking, grounded reasoning, and contextual question-answering in a multimodal environment with rich image and video content. In this demo, we introduce Task-oriented Multimodal Agent Dialogue (TaskMAD), a new platform that supports the creation of interactive multimodal and task-centric datasets in a Wizard-of-Oz experimental setup. TaskMAD includes support for text and voice, federated retrieval from text and knowledge bases, and structured logging of interactions for offline labeling. Its architecture supports a spectrum of tasks that span open-domain exploratory search to traditional frame-based dialogue tasks. It's open-source and offers rich capability as a platform used to collect data for the Amazon Alexa Prize Taskbot challenge, TREC Conversational Assistance track, undergraduate student research, and others. TaskMAD is distributed under the MIT license.
eTrust: Understanding trust evolution in an online world
Authors: Jiliang Tang, Huiji Gao, Huan Liu, Atish Das Sarma
Venue: Conference KDD 2012
Year: 2012

Most existing research about online trust assumes static trust relations between users. As we are informed by social sciences, trust evolves as humans interact. Little work exists studying trust evolution in an online world. Researching online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this paper, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we propose a framework of evolution trust, eTrust, which exploits the dynamics of user preferences in the context of online product review. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as rating and trust prediction.
Locker: Locally Constrained Self-Attentive Sequential Recommendation
Authors: Zhankui He, Handong Zhao, Zhe Lin, Zhaowen Wang, Ajinkya Kale, Julian J. McAuley
Venue: Conference CIKM 2021
Year: 2021

Recently, self-attentive models have shown promise in sequential recommendation, given their potential to capture user long-term preferences and short-term dynamics simultaneously. Despite their success, we argue that self-attention modules, as a non-local operator, often fail to capture short-term user dynamics accurately due to a lack of inductive local bias. To examine our hypothesis, we conduct an analytical experiment on controlled 'short-term' scenarios. We observe a significant performance gap between self-attentive recommenders with and without local constraints, which implies that short-term user dynamics are not sufficiently learned by existing self-attentive recommenders. Motivated by this observation, we propose a simple framework, (Locker) for self-attentive recommenders in a plug-and-play fashion. By combining the proposed local encoders with existing global attention heads, Locker enhances short-term user dynamics modeling, while retaining the long-term semantics captured by standard self-attentive encoders. We investigate Locker with five different local methods, outperforming state-of-the-art self-attentive recom- menders on three datasets by 17.19% (NDCG@20) on average.
Constructing tree-based index for efficient and effective dense retrieval
Authors: Haitao Li, Qingyao Ai, Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Zheng Liu, Zhao Cao
Venue: Conference SIGIR 2023
Year: 2023

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that are difficult to be pre-processed with most existing search indexing systems. To avoid the expensive cost of brute-force search, the Approximate Nearest Neighbor (ANN) algorithm and corresponding indexes are widely applied to speed up the inference process of DR models. Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. Specifically, we design a new unified contrastive learning loss to train tree-based index and query encoder in an end-to-end manner. The tree-based negative sampling strategy is applied to make the tree have the maximum heap property, which supports the effectiveness of beam search well. Moreover, we treat the cluster assignment as an optimization problem to update the tree-based index that allows overlapped clustering. We evaluate JTR on numerous popular retrieval benchmarks. Experimental results show that JTR achieves better retrieval performance while retaining high system efficiency compared with widely-adopted baselines. It provides a potential solution to balance efficiency and effectiveness in neural retrieval system designs.
Investigating user behavior in legal case retrieval
Authors: Yunqiu Shao, Yueyue Wu, Yiqun Liu 0001, Jiaxin Mao, Min Zhang 0006, Shaoping Ma
Venue: Conference SIGIR 2021
Year: 2021

Legal case retrieval is a specialized IR task aiming to retrieve supporting cases given a query case. While recent research efforts are committed to improving the automatic retrieval models' performances, little attention has been paid to the practical search interactions between users and systems in this task. Therefore, we focus on investigating user behavior in the scenario of legal case retrieval. Specifically, we conducted a laboratory user study that involved 45 participants majoring in law to collect users' rich interactions and relevance assessments. With the collected data, we first analyzed the characteristics of the search process in legal case retrieval practice. We observed significant differences between legal case retrieval and general web search in various search behavior. These differences highlight the necessity of in-depth investigating user behavior in legal case retrieval and re-thinking the application of related mechanisms developed based on the user models in Web search. Then we investigated factors that would influence search behavior from different perspectives, including task difficulty and domain expertise. Finally, we shed light on implicit feedback in legal case retrieval and designed a predictive model for relevance based on user behavior. Our work provides a better understanding of user interactions in the legal case retrieval process, which can benefit the design of the corresponding retrieval systems to support legal practitioners.
Circumstances enhanced criminal court view generation
Authors: Linan Yue, Qi Liu 0003, Han Wu 0002, Yanqing An, Li Wang 0014, Senchao Yuan, Dayong Wu
Venue: Conference SIGIR 2021
Year: 2021

Criminal Court View Generation is an essential task in legal intelligence, which aims to automatically generate sentences interpreting judgment results. The court view could be seen as the summary of crime circumstances in a case, including ADjudging Circumstance (ADC) and SEntencing Circumstance (SEC). However, different circumstances vary widely, and adopting them to generate court views directly may limit the generation performance. Therefore, it is necessary to identify the ADC and SEC related sentences in case facts and enhance them into the court view generation, respectively. To this end, in this paper, we propose a novel Circumstances enhanced Criminal Court View Generation (C3VG) method, consisting of the extraction and generation stage. Specifically, in the extraction stage, we design a Circumstances Selector to select ADC and SEC related sentences. After that, we apply them to two generators to generate the circumstances enhanced court views, respectively. After merging the two types of court views, we could obtain the final court views. We evaluate C3VG by conducting extensive experiments on a real-world dataset and experimental results clearly validate the effectiveness of our proposed model.
Interactive retrieval based on faceted feedback
Authors: Lanbo Zhang, Yi Zhang
Venue: Conference SIGIR 2010
Year: 2010

Motivated by the commonly used faceted search interface in e-commerce, this paper investigates interactive relevance feedback mechanism based on faceted document metadata. In this mechanism, the system recommends a group of document facet-value pairs, and lets users select relevant ones to restrict the returned documents. We propose four facet-value pair recommendation approaches and two retrieval models that incorporate user feedback on document facets. Evaluated based on user feedback collected through Amazon Mechanical Turk, our experimental results show that the Boolean filtering approach, which is widely used in faceted search in e-commerce, doesn't work well for text document retrieval, due to the incompleteness (low recall) of metadata assignment in semi-structured text documents. Instead, a soft model performs more effectively. The faceted feedback mechanism can also be combined with document-based relevance feedback and pseudo relevance feedback to further improve the retrieval performance.
Privacy protection in deep multi-modal retrieval
Authors: Peng-Fei Zhang, Yang Li 0140, Zi Huang, Hongzhi Yin
Venue: Conference SIGIR 2021
Year: 2021

Deep learning techniques have ushered in significant progress in large-scale multi-modal retrieval. Nevertheless, the advanced techniques may be used nefariously to conduct a search that violates the privacy of individuals. In this paper, we propose a novel PrIvacy Protection method (PIP) against malicious multi-modal retrieval models, which proactively transfers original data into adversarial data with quasi-imperceptible perturbations before releasing them. Consequently, unauthorized malicious parties are not able to use deployed deep models to find out desired sensitive information with them. In addition to privacy preserving, PIP synchronously learns an effective multi-modal retrieval model to facilitate authorized uses, endowed with strong resilience to the perturbations. To the best of our knowledge, it is a very first attempt to consider privacy issues in multi-modal retrieval, and encapsulate both privacy protection against unauthorized retrieval and robust multi-modal learning for authorized uses into a unified framework. This work is conducted in the challenging no-box and unsupervised settings, where neither target malicious models nor supervised information is known. The optimization objective of our versatile PIP is achieved through a two-player game between different components with both the intra- and inter-modality graph alignments and the domain distribution alignment considered. Besides, a high-level similarity matrix is developed to obtain reliable guidance for learning. Empirically, we apply the proposed PIP to hashing based multi-modal retrieval scenarios and prove its effectiveness on a range of benchmarks and tasks.
Disentangling past-future modeling in sequential recommendation via dual networks
Authors: Hengyu Zhang, Enming Yuan, Wei Guo, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Xiu Li, Ruiming Tang
Venue: Conference CIKM 2022
Year: 2022

Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors. Unlike the standard autoregressive training strategy, future data (also available during training) has been used to facilitate model training as it provides richer signals about users' current interests and can be used to improve the recommendation quality. However, existing methods suffer from a severe training-inference gap, i.e., both past and future contexts are modeled by the same encoder when training, while only historical behaviors are available during inference. This discrepancy leads to potential performance degradation. To alleviate the training-inference gap, we propose a new framework DualRec, which achieves past-future disentanglement and past-future mutual enhancement by a novel dual network. Specifically, a dual network structure is exploited to model the past and future context separately.And a bi-directional knowledge transferring mechanism enhances the knowledge learnt by the dual network. Extensive experiments on four real-world datasets demonstrate the superiority of our approach over baseline methods. Besides, we demonstrate the compatibility of DualRec by instantiating using different backbones. Further empirical analysis verifies the high utility of modeling future contexts under our DualRec framework.
Stochastic Optimization of Text Set Generation for Learning Multiple Query Intent Representations
Authors: Helia Hashemi, Hamed Zamani, W. Bruce Croft
Venue: Conference CIKM 2022
Year: 2022

Learning multiple intent representations for queries has potential applications in facet generation, document ranking, search result diversification, and search explanation. The state-of-the-art model for this task assumes that there is a sequence of intent representations. In this paper, we argue that the model should not be penalized as long as it generates an accurate and complete set of intent representations. Based on this intuition, we propose a stochastic permutation invariant approach for optimizing such networks. We extrinsically evaluate the proposed approach on a facet generation task and demonstrate significant improvements compared to competitive baselines. Our analysis shows that the proposed permutation invariant approach has the highest impact on queries with more potential intents.
Structural inference for semistructured data
Authors: Jason Sankey, Raymond K. Wong
Venue: Conference CIKM 2001
Year: 2001

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