Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第4页

Explainable Information Retrieval 可解释的信息检索

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3594249

Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, Mandar Mitra

{"title":"Explainable Information Retrieval","authors":"Avishek Anand, Procheta Sen, Sourav Saha, Manisha Verma, Mandar Mitra","doi":"10.1145/3539618.3594249","DOIUrl":"https://doi.org/10.1145/3539618.3594249","url":null,"abstract":"This tutorial presents explainable information retrieval (ExIR), an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4-5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in ExIR, while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will delve into method families and their adaptations to IR, extensively covering post-hoc methods, axiomatic and probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, patent and legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on applying ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124127646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries 噪声保护:一种鲁棒化视觉推荐的方法

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591971

Felice Antonio Merra, V. W. Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino

引用次数: 1

Multi-view Multi-aspect Neural Networks for Next-basket Recommendation 下一篮推荐的多视图多面向神经网络

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591738

Zhiying Deng, Jianjun Li, Zhiqiang Guo, Wei Liu, Li Zou, Guohui Li

{"title":"Multi-view Multi-aspect Neural Networks for Next-basket Recommendation","authors":"Zhiying Deng, Jianjun Li, Zhiqiang Guo, Wei Liu, Li Zou, Guohui Li","doi":"10.1145/3539618.3591738","DOIUrl":"https://doi.org/10.1145/3539618.3591738","url":null,"abstract":"Next-basket recommendation (NBR) is a type of recommendation that aims to recommend a set of items to users according to their historical basket sequences. Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. Then, to capture low-level item correlations, MMNR models different aspects of items to obtain disentangled representations of items, thereby fully capturing multiple user interests. Extensive experiments on real-world datasets demonstrate the effectiveness of MMNR, showing that it consistently outperforms several state-of-the-art NBR methods.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation 面向知识图感知的混合曲率流形交互学习

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591730

Jihu Wang, Yuliang Shi, Han Yu, Xinjun Wang, Zhongmin Yan, Fanyu Kong

{"title":"Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation","authors":"Jihu Wang, Yuliang Shi, Han Yu, Xinjun Wang, Zhongmin Yan, Fanyu Kong","doi":"10.1145/3539618.3591730","DOIUrl":"https://doi.org/10.1145/3539618.3591730","url":null,"abstract":"As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of recommendation tasks. Thus many works consider obtaining user and item representations via information aggregation on graph-structured data within Euclidean space. However, the scale-free graphs (e.g., KGs) inherently exhibit non-Euclidean geometric topologies, such as tree-like and circle-like structures. The existing recommendation models built in a single type of embedding space do not have enough capacity to embrace various geometric patterns, consequently, resulting in suboptimal performance. To address this limitation, we propose a KG-aware recommendation model with mixed-curvature manifolds interaction learning, namely CurvRec. On the one hand, it aims to preserve various global geometric structures in KG with mixed-curvature manifold spaces as the backbone. On the other hand, we integrate Ricci curvature into graph convolutional networks (GCNs) to capture local geometric structural properties when aggregating neighbor nodes. Besides, to exploit the expressive spatial features in KG, we incorporate interaction learning to ensure the geometric message passing between curved manifolds. Specifically, we adopt curvature-aware geodesic distance metrics to maximize the mutual information between Euclidean space and non-Euclidean spaces. Through extensive experiments, we demonstrate that the proposed CurvRec outperforms state-of-the-art baselines.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116246852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Disentangling User Conversations with Voice Assistants for Online Shopping 用语音助手解开在线购物的用户对话

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591974

Nikhita Vedula, M. Collins, Oleg Rokhlenko

{"title":"Disentangling User Conversations with Voice Assistants for Online Shopping","authors":"Nikhita Vedula, M. Collins, Oleg Rokhlenko","doi":"10.1145/3539618.3591974","DOIUrl":"https://doi.org/10.1145/3539618.3591974","url":null,"abstract":"Conversation disentanglement aims to identify and group utterances from a conversation into separate threads. Existing methods primarily focus on disentangling multi-party conversations with three or more speakers, explicitly or implicitly incorporating speaker-related feature signals to disentangle. Most existing models require a large amount of human annotated data for model training, and often focus on pairwise relations between utterances, not accounting much for the conversational context. In this work, we propose a multi-task learning approach with a contrastive learning objective, DiSC, to disentangle conversations between two speakers -- a user and a virtual speech assistant, for a novel domain of e-commerce. We analyze multiple ways and granularities to define conversation \"threads''. DiSC jointly learns the relation between pairs of utterances, as well as between utterances and their respective thread context. We train and evaluate our models on multiple multi-threaded conversation datasets that were automatically created, without any human labeling effort. Experimental results on public datasets as well as real-world shopping conversations from a commercial speech assistant show that DiSC outperforms state-of-the-art baselines by at least 3%, across both automatic and human evaluation metrics. We also demonstrate how DiSC improves downstream dialog response generation in the shopping domain.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116426046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

ExaRanker: Synthetic Explanations Improve Neural Rankers ExaRanker:综合解释提高神经排序器

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592067

Fernando Ferraretto, Thiago Laitz, R. Lotufo, Rodrigo Nogueira

引用次数: 0

How Well do Offline Metrics Predict Online Performance of Product Ranking Models? 线下指标如何很好地预测产品排名模型的在线表现?

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591865

Xiaojie Wang, Ruoyuan Gao, Anoop Jain, Graham Edge, Sachin Ahuja

{"title":"How Well do Offline Metrics Predict Online Performance of Product Ranking Models?","authors":"Xiaojie Wang, Ruoyuan Gao, Anoop Jain, Graham Edge, Sachin Ahuja","doi":"10.1145/3539618.3591865","DOIUrl":"https://doi.org/10.1145/3539618.3591865","url":null,"abstract":"Online evaluation techniques are widely adopted by industrial search engines to determine which ranking models perform better under a certain business metric. However, online evaluation can only evaluate a small number of rankers and people resort to offline evaluation to select rankers that are likely to yield good online performance. To use offline metrics for effective model selection, a major challenge is to understand how well offline metrics predict which ranking models perform better in online experiments. This paper aims to address this challenge in product search ranking. Towards this end, we collect gold data in the form of preferences over ranker pairs under a business metric in e-commerce search engine. For the first time, we use such gold data to evaluate offline metrics in terms of directional agreement with the business metric. Furthermore, we analyze offline metrics in terms of discriminative power through paired sample t-test and rank correlations among offline metrics. Through extensive online and offline experiments, we studied 36 offline metrics and observed that: (1) Offline metrics align well with online metrics: they agree on which one of two ranking models is better up to 97% of times; (2) Offline metrics are highly discriminative on large-scale search ranking data, especially NDCG (Normalized Discounted Cumulative Gain) which has a discriminative power over 99%.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117157031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Exploring User and Item Representation, Justification Generation, and Data Augmentation for Conversational Recommender Systems 探索会话推荐系统的用户和项目表示，证明生成和数据增强

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591795

Sergey Volokhin

{"title":"Exploring User and Item Representation, Justification Generation, and Data Augmentation for Conversational Recommender Systems","authors":"Sergey Volokhin","doi":"10.1145/3539618.3591795","DOIUrl":"https://doi.org/10.1145/3539618.3591795","url":null,"abstract":"Conversational Recommender Systems (CRS) aim to provide personalized and contextualized recommendations through natural language conversations with users. The objective of my proposed dissertation is to capitalize on the recent developments in conversational interfaces to advance the field of Recommender Systems in several directions. I aim to address several problems in recommender systems: user and item representation, justification generation, and data sparsity. A critical challenge in CRS is learning effective representations of users and items that capture their preferences and characteristics. First, we focus on user representation, where we use a separate corpus of reviews to learn user representation. We attempt to map conversational users into the space of reviewers using semantic similarity between the conversation and the texts of reviews. Second, we improve item representation by incorporating textual features such as item descriptions into the user-item interaction graph, which captures a great deal of semantic and behavioral information unavailable from the purely topological structure of the interaction graph. Justifications for recommendations enhance the explainability and transparency of CRS; however, existing approaches, such as rule-based and template-based methods, have limitations. In this work, we propose an extractive method using a corpus of reviews to identify relevant information for generating concise and coherent justifications. We address the challenge of data scarcity for CRS by generating synthetic conversations using SOTA generative pre trained transformers (GPT). These synthetic conversations are used to augment the data used for training the CRS. In addition, we also evaluate if the GPTs exhibit emerging abilities of CRS (or a non-conversational RecSys) due to the large amount of data they are trained on, which potentially includes the reviews and opinions of users.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129624433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Type Enhanced Negative Sampling for Knowledge Graph Embedding 层次型增强负抽样知识图嵌入

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591996

Zhenzhou Lin, Zishuo Zhao, Jingyou Xie, Ying Shen

引用次数: 0

Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features 展望未来:通过预览未来时间特征来提高时间序列预测的准确性

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592013

Seonmin Kim, Dong-Kyu Chae

{"title":"Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features","authors":"Seonmin Kim, Dong-Kyu Chae","doi":"10.1145/3539618.3592013","DOIUrl":"https://doi.org/10.1145/3539618.3592013","url":null,"abstract":"Time-series forecasting has been actively studied and adopted in various real-world domains. Recently there have been two research mainstreams in this area: building Transformer-based architectures such as Informer, Autoformer and Reformer, and developing time-series representation learning frameworks based on contrastive learning such as TS2Vec and CoST. Both efforts have greatly improved the performance of time series forecasting. In this paper, we investigate a novel direction towards improving the forecasting performance even more, which is orthogonal to the aforementioned mainstreams as a model-agnostic scheme. We focus on time stamp embeddings that has been less-focused in the literature. Our idea is simple-yet-effective: based on given current time stamp, we predict embeddings of its near future time stamp and utilize the predicted embeddings in the time-series (value) forecasting task. We believe that if such future time information can be previewed at the time of prediction, they can be utilized by any time-series forecasting models as useful additional information. Our experimental results confirmed that our method consistently and significantly improves the accuracy of the recent Transformer-based models and time-series representation learning frameworks. Our code is available at: https://github.com/sunsunmin/Look_Ahead","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122345335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0