Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval最新文献_第5页

News Article Retrieval in Context for Event-centric Narrative Creation 以事件为中心的新闻文章检索

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-06-30 DOI: 10.1145/3471158.3472247

Nikos Voskarides, S. Sauer

引用次数: 2

A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models 基于深度生成检索模型的查询似然的现代视角

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-06-25 DOI: 10.1145/3471158.3472229

Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, M. Schedl

{"title":"A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models","authors":"Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, M. Schedl","doi":"10.1145/3471158.3472229","DOIUrl":"https://doi.org/10.1145/3471158.3472229","url":null,"abstract":"Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval models, we introduce and formalize the paradigm of deep generative retrieval models defined via the cumulative probabilities of generating query terms. This paradigm offers a grounded probabilistic view on relevance estimation while still enabling the use of modern neural architectures. In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty. We adopt several current neural generative models in our framework and introduce a novel generative ranker T-PGN, which combines the encoding capacity of Transformers with the Pointer Generator Network model. We conduct an extensive set of evaluation experiments on passage retrieval, leveraging the MS MARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking collections. Our results show the significantly higher performance of the T-PGN model when compared with other generative models. Lastly, we demonstrate that exploiting the uncertainty information of deep generative rankers opens new perspectives to query/collection understanding, and significantly improves the cut-off prediction task.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115813855","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: 12

Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval 多表示密集检索的伪相关反馈

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-06-21 DOI: 10.1145/3471158.3472250

Xiao Wang, C. Macdonald, N. Tonellotto, I. Ounis

{"title":"Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval","authors":"Xiao Wang, C. Macdonald, N. Tonellotto, I. Ounis","doi":"10.1145/3471158.3472250","DOIUrl":"https://doi.org/10.1145/3471158.3472250","url":null,"abstract":"Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have shown the usefulness of expanding and reweighting the users' initial queries using information occurring in an initial set of retrieved documents, known as the pseudo-relevant set. Recently, dense retrieval -- through the use of neural contextual language models such as BERT for analysing the documents' and queries' contents and computing their relevance scores -- has shown a promising performance on several information retrieval tasks still relying on the traditional inverted index for identifying documents relevant to a query. Two different dense retrieval families have emerged: the use of single embedded representations for each passage and query (e.g. using BERT's [CLS] token), or via multiple representations (e.g. using an embedding for each token of the query and document). In this work, we conduct the first study into the potential for multiple representation dense retrieval to be enhanced using pseudo-relevance feedback. In particular, based on the pseudo-relevant set of documents identified using a first-pass dense retrieval, we extract representative feedback embeddings (using KMeans clustering) -- while ensuring that these embeddings discriminate among passages (based on IDF) -- which are then added to the query representation. These additional feedback embeddings are shown to both enhance the effectiveness of a reranking as well as an additional dense retrieval operation. Indeed, experiments on the MSMARCO passage ranking dataset show that MAP can be improved by upto 26% on the TREC 2019 query set and 10% on the TREC 2020 query set by the application of our proposed ColBERT-PRF method on a ColBERT dense retrieval approach.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122649199","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: 45

Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation 了解电子商务Top-N推荐中评论的有效性

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-06-17 DOI: 10.1145/3471158.3472258

Zhichao Xu, Hansi Zeng, Qingyao Ai

{"title":"Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation","authors":"Zhichao Xu, Hansi Zeng, Qingyao Ai","doi":"10.1145/3471158.3472258","DOIUrl":"https://doi.org/10.1145/3471158.3472258","url":null,"abstract":"Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommendation. Through textual reviews, a user explicitly express her affinity towards the item. Previous researchers found that by using the information extracted from these reviews, we can better profile the users' explicit preferences as well as the item features, leading to the improvement of recommendation performance. However, most of the previous algorithms were only utilizing the review information for explicit-feedback problem i.e. rating prediction, and when it comes to implicit-feedback ranking problem such as top-N recommendation, the usage of review information has not been fully explored. Seeing this gap, in this work, we investigate the effectiveness of textual review information for top-N recommendation under E-commerce settings. We adapt several SOTA review-based rating prediction models for top-N recommendation tasks and compare them to existing top-N recommendation models from both performance and efficiency. We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method. When utilizing review information as a regularizer or auxiliary information, the performance of implicit-feedback matrix factorization method can be further improved. However, the optimal model structure to utilize textual reviews for E-commerce top-N recommendation is yet to be determined.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600425","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: 5

Towards Axiomatic Explanations for Neural Ranking Models 神经排序模型的公理解释

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2021-06-15 DOI: 10.1145/3471158.3472256

Michael Völske, Alexander Bondarenko, Maik Fröbe, Matthias Hagen, Benno Stein, Jaspreet Singh, Avishek Anand

{"title":"Towards Axiomatic Explanations for Neural Ranking Models","authors":"Michael Völske, Alexander Bondarenko, Maik Fröbe, Matthias Hagen, Benno Stein, Jaspreet Singh, Avishek Anand","doi":"10.1145/3471158.3472256","DOIUrl":"https://doi.org/10.1145/3471158.3472256","url":null,"abstract":"Recently, neural networks have been successfully employed to improve upon state-of-the-art effectiveness in ad-hoc retrieval tasks via machine-learned ranking functions. While neural retrieval models grow in complexity and impact, little is understood about their correspondence with well-studied IR principles. Recent work on interpretability in machine learning has provided tools and techniques to understand neural models in general, yet there has been little progress towards explaining ranking models. We investigate whether one can explain the behavior of neural ranking models in terms of their congruence with well understood principles of document ranking by using established theories from axiomatic~IR. Axiomatic analysis of information retrieval models has formalized a set of constraints on ranking decisions that reasonable retrieval models should fulfill. We operationalize this axiomatic thinking to reproduce rankings based on combinations of elementary constraints. This allows us to investigate to what extent the ranking decisions of neural rankers can be explained in terms of the existing retrieval axioms, and which axioms apply in which situations. Our experimental study considers a comprehensive set of axioms over several representative neural rankers. While the existing axioms can already explain the particularly confident ranking decisions rather well, future work should extend the axiom set to also cover the other still \"unexplainable\" neural IR rank decisions.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674358","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: 18

User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets 双边市场中用户公平、项目公平与排名多样性

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval Pub Date : 2020-10-04 DOI: 10.1145/3471158.3472260

Lequn Wang, T. Joachims

{"title":"User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets","authors":"Lequn Wang, T. Joachims","doi":"10.1145/3471158.3472260","DOIUrl":"https://doi.org/10.1145/3471158.3472260","url":null,"abstract":"Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users --- not just the majority group --- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works. However, we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a novel Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we investigate empirically on a new benchmark dataset how effectively the proposed ranking algorithm can control user fairness, item fairness and diversity, as well as the trade-offs between them.","PeriodicalId":289271,"journal":{"name":"Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114469580","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: 36