Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第6页

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

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401272

E. Nouri, Robert Sim, Adam Fourney, Ryen W. White

引用次数: 3

MaHRL

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401170

Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng P. Yan

{"title":"MaHRL","authors":"Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng P. Yan","doi":"10.1145/3397271.3401170","DOIUrl":"https://doi.org/10.1145/3397271.3401170","url":null,"abstract":"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.","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":"115094095","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

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

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401197

Anirban Chakraborty, Debasis Ganguly, Owen Conlan

引用次数: 10

DVGAN

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401084

Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-rong Wen

{"title":"DVGAN","authors":"Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-rong Wen","doi":"10.1145/3397271.3401084","DOIUrl":"https://doi.org/10.1145/3397271.3401084","url":null,"abstract":"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.","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":"114185903","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: 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

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401142

Ziyang Wang, Wei Wei, G. Cong, Xiaoli Li, Xian-Ling Mao, Minghui Qiu

{"title":"Global Context Enhanced Graph Neural Networks for Session-based Recommendation","authors":"Ziyang Wang, Wei Wei, G. Cong, Xiaoli Li, Xian-Ling Mao, Minghui Qiu","doi":"10.1145/3397271.3401142","DOIUrl":"https://doi.org/10.1145/3397271.3401142","url":null,"abstract":"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.","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":"114232151","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: 293

Query by Example for Cross-Lingual Event Retrieval 跨语言事件检索的示例查询

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401283

Sheikh Muhammad Sarwar, J. Allan

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

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401171

Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie

{"title":"Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs","authors":"Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie","doi":"10.1145/3397271.3401171","DOIUrl":"https://doi.org/10.1145/3397271.3401171","url":null,"abstract":"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.","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":"123068795","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: 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

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401414

Minghui Yang, Hengbin Cui, Shaosheng Cao, Yafang Wang, Xiaolong Li

引用次数: 2

Personalized Query Suggestions 个性化查询建议

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401331

Jianling Zhong, Weiwei Guo, Huiji Gao, Bo Long

{"title":"Personalized Query Suggestions","authors":"Jianling Zhong, Weiwei Guo, Huiji Gao, Bo Long","doi":"10.1145/3397271.3401331","DOIUrl":"https://doi.org/10.1145/3397271.3401331","url":null,"abstract":"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.","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":"124937221","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

HME HME

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI: 10.1145/3397271.3401049

Shanshan Feng, Lucas Vinh Tran, G. Cong, Lisi Chen, Jing Li, Fan Li

{"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