ACM Transactions on Information Systems (TOIS)最新文献_第7页

Multi-interest Diversification for End-to-end Sequential Recommendation 端到端顺序推荐的多利益多样化

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-09-07 DOI: 10.1145/3475768

Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, Maarten de Rijke

{"title":"Multi-interest Diversification for End-to-end Sequential Recommendation","authors":"Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, Maarten de Rijke","doi":"10.1145/3475768","DOIUrl":"https://doi.org/10.1145/3475768","url":null,"abstract":"Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation. Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"56 1","pages":"1 - 30"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82758840","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: 21

A Review on Question Generation from Natural Language Text 自然语言文本问题生成研究综述

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-09-07 DOI: 10.1145/3468889

Ruqing Zhang, Jiafeng Guo, Luyao Chen, Yixing Fan, Xueqi Cheng

{"title":"A Review on Question Generation from Natural Language Text","authors":"Ruqing Zhang, Jiafeng Guo, Luyao Chen, Yixing Fan, Xueqi Cheng","doi":"10.1145/3468889","DOIUrl":"https://doi.org/10.1145/3468889","url":null,"abstract":"Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"54 1","pages":"1 - 43"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86620037","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: 26

Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting 多阶段会话段落检索:一种融合词重要性估计和神经查询重写的方法

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-31 DOI: 10.1145/3446426

Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy J. Lin

{"title":"Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting","authors":"Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy J. Lin","doi":"10.1145/3446426","DOIUrl":"https://doi.org/10.1145/3446426","url":null,"abstract":"Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this article, we tackle conversational passage retrieval, an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad hoc IR system. Specifically, we propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, stand-alone, human-understandable queries with a pretrained sequence-to-sequence model. Detailed analyses of the two CQR methods are provided quantitatively and qualitatively, explaining their advantages, disadvantages, and distinct behaviors. Moreover, to leverage the strengths of both CQR methods, we propose combining their output with reciprocal rank fusion, yielding state-of-the-art retrieval effectiveness, 30% improvement in terms of NDCG@3 compared to the best submission of Text REtrieval Conference (TREC) Conversational Assistant Track (CAsT) 2019.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"3 1","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87778704","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: 41

Conversational Search and Recommendation: Introduction to the Special Issue 会话搜索和推荐:特刊导论

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-31 DOI: 10.1145/3465272

C. Hauff, Julia Kiseleva, M. Sanderson, Hamed Zamani, Yongfeng Zhang

{"title":"Conversational Search and Recommendation: Introduction to the Special Issue","authors":"C. Hauff, Julia Kiseleva, M. Sanderson, Hamed Zamani, Yongfeng Zhang","doi":"10.1145/3465272","DOIUrl":"https://doi.org/10.1145/3465272","url":null,"abstract":"While conversational search and recommendation has roots in early Information Retrieval (IR) research, the recent advances in automatic voice recognition and conversational agents have created increasing interest in this area. This topic was recognized as an emerging research area in the Third Strategic Workshop on Information Retrieval in Lorne (SWIRL 2018) [Culpepper et al. 2018]. Conversational search and recommendation systems consist of multiple components, from user modeling to conversational understanding to query modeling to result presentation. In recent years, the IR and related communities have witnessed a number of major contributions to the field of conversational search and recommendation. They include but are not limited to conversational search conceptualization (e.g., Azzopardi et al. [2018], Deldjoo et al. [2021], and Radlinski and Craswell [2017]), effective conversational query re-writing (e.g., Yu et al. [2020]), generating and selecting clarifying questions (e.g., Zamani et al. [2020a, c]), conversational preference elicitation (e.g., Radlinski et al. [2019] and Zhang et al. [2018]), and understanding user interactions with spoken conversational systems (e.g., Trippas et al. [2018, 2020]). The growing body of work in this area has been supplemented by an increasing number of recent seminars (e.g., Anand et al. [2020]), workshops (e.g., Arguello et al. [2018], Burtsev et al. [2017], Chuklin et al. [2018], and","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"1 1","pages":"1 - 6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79811069","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: 7

How Am I Doing?: Evaluating Conversational Search Systems Offline 我做得怎么样?:离线评估会话搜索系统

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-17 DOI: 10.1145/3451160

Aldo Lipani, Ben Carterette, Emine Yilmaz

{"title":"How Am I Doing?: Evaluating Conversational Search Systems Offline","authors":"Aldo Lipani, Ben Carterette, Emine Yilmaz","doi":"10.1145/3451160","DOIUrl":"https://doi.org/10.1145/3451160","url":null,"abstract":"As conversational agents like Siri and Alexa gain in popularity and use, conversation is becoming a more and more important mode of interaction for search. Conversational search shares some features with traditional search, but differs in some important respects: conversational search systems are less likely to return ranked lists of results (a SERP), more likely to involve iterated interactions, and more likely to feature longer, well-formed user queries in the form of natural language questions. Because of these differences, traditional methods for search evaluation (such as the Cranfield paradigm) do not translate easily to conversational search. In this work, we propose a framework for offline evaluation of conversational search, which includes a methodology for creating test collections with relevance judgments, an evaluation measure based on a user interaction model, and an approach to collecting user interaction data to train the model. The framework is based on the idea of “subtopics”, often used to model novelty and diversity in search and recommendation, and the user model is similar to the geometric browsing model introduced by RBP and used in ERR. As far as we know, this is the first work to combine these ideas into a comprehensive framework for offline evaluation of conversational search.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"10 1","pages":"1 - 22"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87240254","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: 33

Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues 基于检索的对话中多类型深度交互表示的响应排序

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-17 DOI: 10.1145/3462207

Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, Dongyan Zhao

{"title":"Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues","authors":"Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, Dongyan Zhao","doi":"10.1145/3462207","DOIUrl":"https://doi.org/10.1145/3462207","url":null,"abstract":"Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a small window around a word) and long-range (e.g., words across the context and the response) dependencies may exist in dialogue data; and (3) the relationship between the context and the response candidate lies in multiple relevant semantic clues or relatively implicit semantic clues in some real cases. However, existing approaches usually encode the dialogue with mono-type representation and the interaction processes between the context and the response candidate are executed in a rather shallow manner, which may lead to an inadequate understanding of dialogue content and hinder the recognition of the semantic relevance between the context and response. To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection. Particularly, we construct different types of representations for utterance–response pairs and deepen them via alternate encoding and interaction. By this means, the model can handle the relation of neighboring elements, phrasal pattern, and long-range dependencies during the representation and make a more accurate prediction through multiple layers of interactions between the context–response pair. Experiment results on three public benchmarks indicate that the proposed model significantly outperforms previous conventional context-response matching models and achieve slightly better results than the BERT model for multi-turn response selection in retrieval-based dialogue systems.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"17 1","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78678774","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

From Users’ Intentions to IF-THEN Rules in the Internet of Things 从用户意图到物联网中的IF-THEN规则

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-16 DOI: 10.1145/3447264

Fulvio Corno, Luigi De Russis, A. M. Roffarello

{"title":"From Users’ Intentions to IF-THEN Rules in the Internet of Things","authors":"Fulvio Corno, Luigi De Russis, A. M. Roffarello","doi":"10.1145/3447264","DOIUrl":"https://doi.org/10.1145/3447264","url":null,"abstract":"In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account (a) the current user’s intention, (b) the connected entities owned by the user, and (c) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference, thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"26 1","pages":"1 - 33"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85498151","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: 17

MyrrorBot: A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services MyrrorBot:一个基于整体用户模型的数字助理，用于个性化访问在线服务

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-16 DOI: 10.1145/3447679

C. Musto, F. Narducci, Marco Polignano, M. Degemmis, P. Lops, G. Semeraro

{"title":"MyrrorBot: A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services","authors":"C. Musto, F. Narducci, Marco Polignano, M. Degemmis, P. Lops, G. Semeraro","doi":"10.1145/3447679","DOIUrl":"https://doi.org/10.1145/3447679","url":null,"abstract":"In this article, we present MyrrorBot, a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, andfood recommendations, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert?) and personalized access to online services (e.g., Play a song I like). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users’ acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"15 1","pages":"1 - 34"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84196722","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

Theories of Conversation for Conversational IR 会话IR的会话理论

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-08-16 DOI: 10.1145/3439869

Paul Thomas, M. Czerwinski, Daniel J. McDuff, Nick Craswell

引用次数: 18

A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems 一种基于图的缓解推荐系统中多方曝光偏差的方法

ACM Transactions on Information Systems (TOIS) Pub Date : 2021-07-07 DOI: 10.1145/3470948

M. Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, B. Mobasher, R. Burke

{"title":"A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems","authors":"M. Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, B. Mobasher, R. Burke","doi":"10.1145/3470948","DOIUrl":"https://doi.org/10.1145/3470948","url":null,"abstract":"Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users’ final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.","PeriodicalId":6934,"journal":{"name":"ACM Transactions on Information Systems (TOIS)","volume":"68 1","pages":"1 - 31"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86060692","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: 28