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Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

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Proactive Suggestion Generation: Data and Methods for Stepwise Task Assistance 主动建议生成:逐步任务协助的数据和方法
E. Nouri, Robert Sim, Adam Fourney, Ryen W. White
Conversational systems such as digital assistants can help users per-form many simple tasks upon request. Looking to the future, these systems will also need to fully support more complex, multi-step tasks (e.g., following cooking instructions), and help users complete those tasks, e.g., via useful and relevant suggestions made during the process. This paper takes the first step towards automatic generation of task-related suggestions. We introduce proactive suggestion generation as a novel task of natural language generation, in which a decision is made to inject a suggestion into an ongoing user dialog and one is then automatically generated. We propose two types of stepwise suggestions: multiple-choice response generation and text generation. We provide several models for each type of suggestion, including binary and multi-class classification, and text generation.
诸如数字助理之类的会话系统可以根据请求帮助用户执行许多简单的任务。展望未来,这些系统还需要完全支持更复杂、多步骤的任务(例如,遵循烹饪指令),并通过在此过程中提出有用和相关的建议来帮助用户完成这些任务。本文向自动生成任务相关建议迈出了第一步。我们将主动建议生成作为自然语言生成的一项新任务引入,在该任务中,决定向正在进行的用户对话中注入建议,然后自动生成建议。我们提出了两种类型的逐步建议:选择题答案生成和文本生成。我们为每种类型的建议提供了几个模型,包括二元分类和多类分类,以及文本生成。
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引用次数: 3
MaHRL
Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng P. Yan
As huge commercial value of the recommender system, there has been growing interest to improve its performance in recent years. The majority of existing methods have achieved great improvement on the metric of click, but perform poorly on the metric of conversion possibly due to its extremely sparse feedback signal. To track this challenge, we design a novel deep hierarchical reinforcement learning based recommendation framework to model consumers' hierarchical purchase interest. Specifically, the high-level agent catches long-term sparse conversion interest, and automatically sets abstract goals for low-level agent, while the low-level agent follows the abstract goals and catches short-term click interest via interacting with real-time environment. To solve the inherent problem in hierarchical reinforcement learning, we propose a novel multi-goals abstraction based deep hierarchical reinforcement learning algorithm (MaHRL). Our proposed algorithm contains three contributions: 1) the high-level agent generates multiple goals to guide the low-level agent in different sub-periods, which reduces the difficulty of approaching high-level goals; 2) different goals share the same state encoder structure and its parameters, which increases the update frequency of the high-level agent and thus accelerates the convergence of our proposed algorithm; 3) an appreciated reward assignment mechanism is designed to allocate rewards in each goal so as to coordinate different goals in a consistent direction. We evaluate our proposed algorithm based on a real-world e-commerce dataset and validate its effectiveness.
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引用次数: 1
Relevance Models for Multi-Contextual Appropriateness in Point-of-Interest Recommendation 兴趣点推荐中多上下文适当性的关联模型
Anirban Chakraborty, Debasis Ganguly, Owen Conlan
Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.
旅行限定词,如“旅行类型”(度假、工作等)、“陪同”(例如,独自一人、朋友、家人等)是潜在的有用信息来源,可用于提高POI推荐在当前上下文(具有给定的这些约束集合)中的有效性。使用这样的信息并不是直截了当的,因为用户关于过去访问过的poi的文本评论并没有明确地包含这样的注释(例如,关于一次酒吧访问的正面评论不包含关于用户是与朋友一起还是独自一人、出差还是度假的信息)。我们建议使用一小部分手工编译的知识资源来预测用户简介中的评论文本与可能的旅行上下文之间的关联。我们证明,将这些信息合并到基于ir的相关性建模框架中可以显著提高POI推荐。
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引用次数: 10
DVGAN
Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-rong Wen
Search result diversification aims to retrieve diverse results to cover as many subtopics related to the query as possible. Recent studies showed that supervised diversification models are able to outperform the heuristic approaches, by automatically learning a diversification function other than using manually designed score functions. The main challenge of training a diversification model is the lack of high-quality training samples. Due to the involvement of dependence between documents in the ranker, it is very hard for training algorithms to select effective positive and negative ranking lists to train a reliable ranking model, given a large number of candidate documents within which different documents are relevant to different subtopics. To tackle this problem, we propose a supervised diversification framework based on Generative Adversarial Network (GAN). It consists of a generator and a discriminator interacting with each other in a minimax game. Specifically, the generator generates more confusing negative samples for the discriminator, and the discriminator sends back complementary ranking signals to the generator. Furthermore, we explicitly exploit subtopics in the generator, whereas focusing on modeling document similarity in the discriminator. Through such a minimax game, we are able to obtain better ranking models by combining ranking signals learned by the generator and the discriminator. Experimental results on the TREC Web Track dataset show that the proposed method can significantly outperform existing diversification methods.
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引用次数: 20
Global Context Enhanced Graph Neural Networks for Session-based Recommendation 基于会话推荐的全局上下文增强图神经网络
Ziyang Wang, Wei Wei, G. Cong, Xiaoli Li, Xian-Ling Mao, Minghui Qiu
Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the other sessions, which may contain both relevant and irrelevant item-transitions to the current session. This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session. Specifically, GCE-GNN learns two levels of item embeddings from session graph and global graph, respectively: (i) Session graph, which is to learn the session-level item embedding by modeling pairwise item-transitions within the current session; and (ii) Global graph, which is to learn the global-level item embedding by modeling pairwise item-transitions over all sessions. In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. We also design a session-level item representation learning layer, which employs a GNN on the session graph to learn session-level item embeddings within the current session. Moreover, GCE-GNN aggregates the learnt item representations in the two levels with a soft attention mechanism. Experiments on three benchmark datasets demonstrate that GCE-GNN outperforms the state-of-the-art methods consistently.
基于会话的推荐(SBR)是一项具有挑战性的任务,其目的是根据匿名行为序列推荐项目。几乎所有现有的SBR解决方案都仅基于当前会话对用户偏好进行建模,而不利用其他会话,这些会话可能包含到当前会话的相关和不相关的项转换。本文提出了一种新的方法,称为全局上下文增强图神经网络(GCE-GNN),以一种更微妙的方式利用所有会话中的项目转换,以更好地推断当前会话的用户偏好。具体来说,GCE-GNN分别从会话图和全局图中学习两个层次的项目嵌入:(i)会话图,通过对当前会话内的成对项目转换建模来学习会话级的项目嵌入;(ii)全局图,通过对所有会话的成对项目转换建模来学习全局级的项目嵌入。在GCE-GNN中,我们提出了一种新的全局级项目表示学习层,该层采用会话感知关注机制递归地整合全局图上每个节点的邻居嵌入。我们还设计了一个会话级项目表示学习层,该层在会话图上使用GNN来学习当前会话中的会话级项目嵌入。此外,GCE-GNN通过软注意机制将学习到的两个层次的项目表征聚合起来。在三个基准数据集上的实验表明,GCE-GNN始终优于最先进的方法。
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引用次数: 293
Query by Example for Cross-Lingual Event Retrieval 跨语言事件检索的示例查询
Sheikh Muhammad Sarwar, J. Allan
We propose a Query by Example (QBE) setting for cross-lingual event retrieval. In this setting, a user describes a query event using example sentences in one language, and a retrieval system returns a ranked list of sentences that describe the query event, but from a corpus in a different language. One challenge in this setting is that a sentence may mention more than one event. Hence, matching the query sentence with document sentence results in a noisy matching. We propose a Semantic Role Labeling (SRL) based approach to identify event spans in sentences and use a state-of-the-art sentence matching model, Sentence BERT (SBERT) to match event spans in queries and documents without any supervision. To evaluate our approach we construct an event retrieval dataset from ACE which is an existing event detection dataset. Experimental results show that it is valuable to predict event spans in queries and documents and our proposed unsupervised approach achieves superior performance compared to Query Likelihood (QL), Relevance Model 3 (RM3) and SBERT.
我们提出了一个跨语言事件检索的实例查询(QBE)设置。在此设置中,用户使用一种语言的示例句子描述查询事件,检索系统返回描述查询事件的句子排序列表,但这些句子来自不同语言的语料库。在这种情况下,一个挑战是一个句子可能会提到多个事件。因此,将查询句子与文档句子进行匹配会导致有噪声的匹配。我们提出了一种基于语义角色标记(SRL)的方法来识别句子中的事件跨度,并使用最先进的句子匹配模型,句子BERT (SBERT)来匹配查询和文档中的事件跨度,而无需任何监督。为了评估我们的方法,我们从ACE构建了一个事件检索数据集,这是一个现有的事件检测数据集。实验结果表明,我们提出的无监督方法在预测查询和文档中的事件跨度方面是有价值的,与查询似然(QL)、关联模型3 (RM3)和SBERT相比,我们提出的无监督方法取得了更好的性能。
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引用次数: 7
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs 利用知识图上的强化推荐推理演示
Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie
Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization problem. Traditional recommendation methods usually adopt brute-force methods to find feasible paths, which results in issues related to convergence and explainability. In this paper, we address these issues by better supervising the path finding process. The key idea is to extract imperfect path demonstrations with minimum labeling efforts and effectively leverage these demonstrations to guide path finding. In particular, we design a demonstration-based knowledge graph reasoning framework for explainable recommendation. We also propose an ADversarial Actor-Critic (ADAC) model for the demonstration-guided path finding. Experiments on three real-world benchmarks show that our method converges more quickly than the state-of-the-art baseline and achieves better recommendation accuracy and explainability.
知识图谱被广泛用于提高推荐的准确性。知识图上的多跳用户-项目连接也赋予了为什么推荐一个项目的推理。然而,路径推理是一个复杂的组合优化问题。传统的推荐方法通常采用蛮力方法寻找可行路径,这导致了收敛性和可解释性问题。在本文中,我们通过更好地监督寻路过程来解决这些问题。关键思想是用最小的标记工作提取不完美的路径演示,并有效地利用这些演示来指导寻路。特别地,我们为可解释推荐设计了一个基于演示的知识图推理框架。我们还提出了一个对抗的行动者-评论家(ADAC)模型,用于演示导向的寻路。在三个现实世界基准上的实验表明,我们的方法比最先进的基线收敛得更快,并且获得了更好的推荐准确性和可解释性。
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引用次数: 79
ServiceGroup: A Human-Machine Cooperation Solution for Group Chat Customer Service ServiceGroup:群聊客服人机协作解决方案
Minghui Yang, Hengbin Cui, Shaosheng Cao, Yafang Wang, Xiaolong Li
With the rapid growth of B2B (Business-to-Business), how to efficiently respond to various customer questions is becoming an important issue. In this scenario, customer questions always involve many aspects of the products, so there are usually multiple customer service agents to response respectively. To improve efficiency, we propose a human-machine cooperation solution called ServiceGroup, where relevant agents and customers are invited into the same group, and the system can provide a series of intelligent functions, including question notification, question recommendation and knowledge extraction. With the assistance of our developed ServiceGroup, the response rate within 15 minutes is improved twice. Until now, our ServiceGroup has already supported thousands of enterprises by means of millions of groups in instant messaging softwares.
随着B2B (Business-to-Business)的快速发展,如何高效地响应客户的各种问题成为一个重要的问题。在这种情况下,客户的问题总是涉及产品的许多方面,因此通常会有多个客服代表分别回答。为了提高效率,我们提出了一种名为ServiceGroup的人机协作解决方案,将相关的座席和客户邀请到同一个组中,系统可以提供一系列智能功能,包括问题通知、问题推荐和知识提取。在我们开发的ServiceGroup的帮助下,15分钟内的响应速度提高了两倍。到目前为止,我们的ServiceGroup已经通过即时通讯软件中的数百万组支持了数千家企业。
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引用次数: 2
Personalized Query Suggestions 个性化查询建议
Jianling Zhong, Weiwei Guo, Huiji Gao, Bo Long
With the exponential growth of information on the internet, users have been relying on search engines for finding the precise documents. However, user queries are often short. The inherent ambiguity of short queries imposes great challenges for search engines to understand user intent. Query suggestion is one key technique for search engines to augment user queries so that they can better understand user intent. In the past, query suggestions have been relying on either term-frequency--based methods with little semantic understanding of the query, or word-embedding--based methods with little personalization efforts. Here, we present a sequence-to-sequence-model--based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally. This capability opens up the opportunity to better understand query semantics and user intent at the same time. As the largest professional network, LinkedIn has the advantage of utilizing a rich amount of accurate member profile information to personalize query suggestions. We applied this framework in the LinkedIn production traffic and showed that personalized query suggestions significantly improved member search experience as measured by key business metrics at LinkedIn.
随着互联网上信息的指数级增长,用户一直依赖搜索引擎来查找精确的文档。然而,用户查询通常很短。短查询固有的模糊性给搜索引擎理解用户意图带来了巨大的挑战。查询建议是搜索引擎增强用户查询以更好地理解用户意图的一项关键技术。过去,查询建议要么依赖于对查询缺乏语义理解的基于词频的方法,要么依赖于缺乏个性化努力的基于词嵌入的方法。在这里,我们提出了一个基于序列到序列模型的查询建议框架,它能够自然地对结构化、个性化特征和非结构化查询文本进行建模。此功能为更好地同时理解查询语义和用户意图提供了机会。作为最大的专业网络,LinkedIn的优势是利用大量准确的会员资料信息来个性化查询建议。我们将这个框架应用到LinkedIn的生产流量中,并通过LinkedIn的关键业务指标显示,个性化查询建议显著改善了会员搜索体验。
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引用次数: 12
HME HME
Shanshan Feng, Lucas Vinh Tran, G. Cong, Lisi Chen, Jing Li, Fan Li
With the increasing popularity of location-aware social media services, next-Point-of-Interest (POI) recommendation has gained significant research interest. The key challenge of next-POI recommendation is to precisely learn users' sequential movements from sparse check-in data. To this end, various embedding methods have been proposed to learn the representations of check-in data in the Euclidean space. However, their ability to learn complex patterns, especially hierarchical structures, is limited by the dimensionality of the Euclidean space. To this end, we propose a new research direction that aims to learn the representations of check-in activities in a hyperbolic space, which yields two advantages. First, it can effectively capture the underlying hierarchical structures, which are implied by the power-law distributions of user movements. Second, it provides high representative strength and enables the check-in data to be effectively represented in a low-dimensional space. Specifically, to solve the next-POI recommendation task, we propose a novel hyperbolic metric embedding (HME) model, which projects the check-in data into a hyperbolic space. The HME jointly captures sequential transition, user preference, category and region information in a unified approach by learning embeddings in a shared hyperbolic space. To the best of our knowledge, this is the first study to explore a non-Euclidean embedding model for next-POI recommendation. We conduct extensive experiments on three check-in datasets to demonstrate the superiority of our hyperbolic embedding approach over the state-of-the-art next-POI recommendation algorithms. Moreover, we conduct experiments on another four online transaction datasets for next-item recommendation to further demonstrate the generality of our proposed model.
{"title":"HME","authors":"Shanshan Feng, Lucas Vinh Tran, G. Cong, Lisi Chen, Jing Li, Fan Li","doi":"10.1145/3397271.3401049","DOIUrl":"https://doi.org/10.1145/3397271.3401049","url":null,"abstract":"With the increasing popularity of location-aware social media services, next-Point-of-Interest (POI) recommendation has gained significant research interest. The key challenge of next-POI recommendation is to precisely learn users' sequential movements from sparse check-in data. To this end, various embedding methods have been proposed to learn the representations of check-in data in the Euclidean space. However, their ability to learn complex patterns, especially hierarchical structures, is limited by the dimensionality of the Euclidean space. To this end, we propose a new research direction that aims to learn the representations of check-in activities in a hyperbolic space, which yields two advantages. First, it can effectively capture the underlying hierarchical structures, which are implied by the power-law distributions of user movements. Second, it provides high representative strength and enables the check-in data to be effectively represented in a low-dimensional space. Specifically, to solve the next-POI recommendation task, we propose a novel hyperbolic metric embedding (HME) model, which projects the check-in data into a hyperbolic space. The HME jointly captures sequential transition, user preference, category and region information in a unified approach by learning embeddings in a shared hyperbolic space. To the best of our knowledge, this is the first study to explore a non-Euclidean embedding model for next-POI recommendation. We conduct extensive experiments on three check-in datasets to demonstrate the superiority of our hyperbolic embedding approach over the state-of-the-art next-POI recommendation algorithms. Moreover, we conduct experiments on another four online transaction datasets for next-item recommendation to further demonstrate the generality of our proposed model.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125553025","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: 74
期刊
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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