Proceedings of the 30th ACM International Conference on Information & Knowledge Management最新文献_第9页

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Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.5040/9781635577068-1215

Ekta Gujral, L. Neves, Evangelos E. Papalexakis, N. Shah

引用次数: 0

Clustering of Conversational Bandits for User Preference Learning and Elicitation 用户偏好学习与启发的会话强盗聚类

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482328

Junda Wu, Canzhe Zhao, Tong Yu, Jingyang Li, Shuai Li

{"title":"Clustering of Conversational Bandits for User Preference Learning and Elicitation","authors":"Junda Wu, Canzhe Zhao, Tong Yu, Jingyang Li, Shuai Li","doi":"10.1145/3459637.3482328","DOIUrl":"https://doi.org/10.1145/3459637.3482328","url":null,"abstract":"Conversational recommender systems elicit user preference via interactive conversational interactions. By introducing conversational key-terms, existing conversational recommenders can effectively reduce the need for extensive exploration in a traditional interactive recommender. However, there are still limitations of existing conversational recommender approaches eliciting user preference via key-terms. First, the key-term data of the items needs to be carefully labeled, which requires a lot of human efforts. Second, the number of the human labeled key-terms is limited and the granularity of the key-terms is fixed, while the elicited user preference is usually from coarse-grained to fine-grained during the conversations. In this paper, we propose a clustering of conversational bandits algorithm. To avoid the human labeling efforts and automatically learn the key-terms with the proper granularity, we online cluster the items and generate meaningful key-terms for the items during the conversational interactions. Our algorithm is general and can also be used in the user clustering when the feedback from multiple users is available, which further leads to more accurate learning and generations of conversational key-terms. We analyze the regret bound of our learning algorithm. In the empirical evaluations, without using any human labeled key-terms, our algorithm effectively generates meaningful coarse-to-fine grained key-terms and performs as well as or better than the state-of-the-art baseline.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116552086","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

CADRE 框架

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.7202/1034287ar

Xiao Yan, Guangchen Ruan, D. Nikolov, M. Hutchinson, Chathuri Peli Kankanamalage, Benjamin Serrette, James McCombs, Alan Walsh, Esen Tuna, Valentin Pentchev

引用次数: 0

MixBERT for Image-Ad Relevance Scoring in Advertising MixBERT在广告中的形象广告相关性评分

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482143

Tan Yu, Xiaokang Li, Jianwen Xie, Ruiyang Yin, Qing Xu, Ping Li

{"title":"MixBERT for Image-Ad Relevance Scoring in Advertising","authors":"Tan Yu, Xiaokang Li, Jianwen Xie, Ruiyang Yin, Qing Xu, Ping Li","doi":"10.1145/3459637.3482143","DOIUrl":"https://doi.org/10.1145/3459637.3482143","url":null,"abstract":"For a good advertising effect, images in the ad should be highly relevant with the ad title. The images in an ad are normally selected from the gallery based on their relevance scores with the ad's title. To ensure the selected images are relevant with the title, a reliable text-image matching model is necessary. The state-of-the-art text- image matching model, cross-modal BERT, only understands the visual content in the image, which is sub-optimal when the image description is available. In this work, we present MixBERT, an adimage relevance scoring model. It models the ad-image relevance by matching the ad title with the image description and visual content. MixBERT adopts a two-stream architecture. It adaptively selects the useful information from noisy image description and suppresses the noise impeding effective matching. To effectively describe the details in visual content of the image, a set of local convolutional features is used as the initial representation of the image. Moreover, to enhance the perceptual capability of our model in key entities which are important to advertising, we upgrade masked language modeling in vanilla BERT to masked key entity modeling. Offline and online experiments demonstrate its effectiveness.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168758","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: 6

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning 用监督对比学习增强面向方面的情感分析

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482096

Bin Liang, Wan-Chen Luo, Xiang Li, Lin Gui, Min Yang, Xiaoqi Yu, Ruifeng Xu

{"title":"Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning","authors":"Bin Liang, Wan-Chen Luo, Xiang Li, Lin Gui, Min Yang, Xiaoqi Yu, Ruifeng Xu","doi":"10.1145/3459637.3482096","DOIUrl":"https://doi.org/10.1145/3459637.3482096","url":null,"abstract":"Most existing aspect-based sentiment analysis (ABSA) research efforts are devoted to extracting the aspect-dependent sentiment features from the sentence towards the given aspect. However, it is observed that about 60% of the testing aspects in commonly used public datasets are unknown to the training set. That is, some sentiment features carry the same polarity regardless of the aspects they are associated with (aspect-invariant sentiment), which props up the high accuracy of existing ABSA models when inevitably inferring sentiment polarities for those unknown testing aspects. Therefore, in this paper, we revisit ABSA from a novel perspective by deploying a novel supervised contrastive learning framework to leverage the correlation and difference among different sentiment polarities and between different sentiment patterns (aspect-invariant/-dependent). This allows improving the sentiment prediction for (unknown) testing aspects in the light of distinguishing the roles of valuable sentiment features. Experimental results on 5 benchmark datasets show that our proposed approach substantially outperforms state-of-the-art baselines in ABSA. We further extend existing neural network-based ABSA models with our proposed framework and achieve improved performance.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127710913","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: 14

Student Can Also be a Good Teacher: Extracting Knowledge from Vision-and-Language Model for Cross-Modal Retrieval 学生也可以成为好老师:从视觉和语言模型中提取知识进行跨模态检索

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482194

Jun Rao, Tao Qian, Shuhan Qi, Yulin Wu, Qing Liao, Xuan Wang

{"title":"Student Can Also be a Good Teacher: Extracting Knowledge from Vision-and-Language Model for Cross-Modal Retrieval","authors":"Jun Rao, Tao Qian, Shuhan Qi, Yulin Wu, Qing Liao, Xuan Wang","doi":"10.1145/3459637.3482194","DOIUrl":"https://doi.org/10.1145/3459637.3482194","url":null,"abstract":"Astounding results from transformer models with Vision-and Language Pretraining (VLP) on joint vision-and-language downstream tasks have intrigued the multi-modal community. On the one hand, these models are usually so huge that make us more difficult to fine-tune and serve real-time online applications. On the other hand, the compression of the original transformer block will ignore the difference in information between modalities, which leads to the sharp decline of retrieval accuracy. In this work, we present a very light and effective cross-modal retrieval model compression method. With this method, by adopting a novel random replacement strategy and knowledge distillation, our module can learn the knowledge of the teacher with nearly the half number of parameters reduction. Furthermore, our compression method achieves nearly 130x acceleration with acceptable accuracy. To overcome the sharp decline in retrieval tasks because of compression, we introduce the co-attention interaction module to reflect the different information and interaction information. Experiments show that a multi-modal co-attention block is more suitable for cross-modal retrieval tasks rather than the source transformer encoder block.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128104166","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: 8

Principled Multi-Aspect Evaluation Measures of Rankings 排名的原则性多方面评价方法

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482287

Maria Maistro, Lucas Chaves Lima, J. Simonsen, C. Lioma

引用次数: 1

Using Knowledge Concept Aggregation towards Accurate Cognitive Diagnosis 利用知识概念聚合实现准确的认知诊断

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482311

Xinping Wang, Caidie Huang, Jinfang Cai, Liangyu Chen

{"title":"Using Knowledge Concept Aggregation towards Accurate Cognitive Diagnosis","authors":"Xinping Wang, Caidie Huang, Jinfang Cai, Liangyu Chen","doi":"10.1145/3459637.3482311","DOIUrl":"https://doi.org/10.1145/3459637.3482311","url":null,"abstract":"Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which is aimed to mine and analyze the level of knowledge for a student in his or her learning process periodically. While a number of approaches and tools have been developed to diagnose the learning states of students, they do not fully learn the relationship between students, exercises and knowledge concepts in the learning system, or do not consider the traits that it is easier to complete diagnosis when focusing on a small part of knowledge concepts rather than all knowledge concepts. To address these limitations, we develop CDGK, a model based artificial neural network to deal with cognitive diagnosis. Our method not only captures non-linear interactions between exercise features, student scores, and their mastery on each knowledge concept, but also performs an aggregation of the knowledge concepts via converting them into graph structure, and only considering the leaf node in the knowledge concept tree, which can reduce the dimension of the model without accuracy loss. In our evaluation on two real-world datasets, CDGK outperforms the state-of-the-art related approaches in terms of accuracy, reasonableness and interpretability.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134518773","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

Collaborative Experts Discovery in Social Coding Platforms 社交编码平台中的协作专家发现

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482074

Roohollah Etemadi, Morteza Zihayat, Kuan Feng, Jason Adelman, E. Bagheri

引用次数: 1

Fine and Coarse Granular Argument Classification before Clustering 聚类前细粒和粗粒参数分类

Proceedings of the 30th ACM International Conference on Information & Knowledge Management Pub Date : 2021-10-26 DOI: 10.1145/3459637.3482431

Lorik Dumani, Tobias Wiesenfeldt, Ralf Schenkel

{"title":"Fine and Coarse Granular Argument Classification before Clustering","authors":"Lorik Dumani, Tobias Wiesenfeldt, Ralf Schenkel","doi":"10.1145/3459637.3482431","DOIUrl":"https://doi.org/10.1145/3459637.3482431","url":null,"abstract":"Computational argumentation and especially argument mining together with retrieval enjoys increasing popularity. In contrast to standard search engines that focus on finding documents relevant to a query, argument retrieval aims at finding the best supporting and attacking premises given a query claim, e.g., from a predefined collection of arguments. Here, a claim is the central part of an argument representing the standpoint of a speaker with the goal to persuade the audience, and a premise serves as evidence to the claim. In addition to the actual retrieval process, existing work has focused on (1) classifying polarities of arguments into supporting or opposing, (2) classifying arguments by their frames (such as economic or environmental), and (3) clustering similar arguments by their meaning to avoid repetitions in the result list. For experiments, either hand-made argument collections or arguments extracted from debate portals were used. In this paper, we extend existing work on argument clustering, making the following contributions: First, we introduce a novel pipeline for clustering arguments. While previous work classified arguments either by polarity, frame, or meaning, our pipeline incorporates these three, allowing a more systematic presentation of arguments. Second, we introduce a new dataset consisting of 365 argument graphs accompanying more than 11,000 high-quality arguments that, contrary to previous datasets, have been generated, displayed, and verified by journalists and were published in newspapers. A thorough evaluation with this dataset provides a first baseline for future work.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"58 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001093","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: 2