Proceedings of the 13th International Conference on Web Search and Data Mining最新文献_第3页

Beyond Sessions: Exploiting Hybrid Contextual Information for Web Search 超越会话:为网络搜索开发混合上下文信息

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3372179

Jia Chen

引用次数: 2

NLP4REC: The WSDM 2020 Workshop on Natural Language Processing for Recommendations NLP4REC: WSDM 2020自然语言处理推荐研讨会

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371884

Pengjie Ren, Z. Ren, Fei Sun, Xiangnan He, Dawei Yin, M. de Rijke

引用次数: 4

ADMM SLIM: Sparse Recommendations for Many Users ADMM SLIM:针对许多用户的稀疏推荐

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371774

H. Steck, Maria Dimakopoulou, Nickolai Riabov, T. Jebara

{"title":"ADMM SLIM: Sparse Recommendations for Many Users","authors":"H. Steck, Maria Dimakopoulou, Nickolai Riabov, T. Jebara","doi":"10.1145/3336191.3371774","DOIUrl":"https://doi.org/10.1145/3336191.3371774","url":null,"abstract":"The Sparse Linear Method (SLIM) is a well-established approach for top-N recommendations. This article proposes several improvements that are enabled by the Alternating Directions Method of Multipliers (ADMM), a well-known optimization method with many application areas. First, we show that optimizing the original SLIM-objective by ADMM results in an approach where the training time is independent of the number of users in the training data, and hence trivially scales to large numbers of users. Second, the flexibility of ADMM allows us to switch on and off the various constraints and regularization terms in the original SLIM-objective, in order to empirically assess their contributions to ranking accuracy on given data. Third, we also propose two extensions to the original SLIM training-objective in order to improve recommendation accuracy further without increasing the computational cost. In our experiments on three well-known data-sets, we first compare to the original SLIM-implementation and find that not only ADMM reduces training time considerably, but also achieves an improvement in recommendation accuracy due to better optimization. We then compare to various state-of-the-art approaches and observe up to 25% improvement in recommendation accuracy in our experiments. Finally, we evaluate the importance of sparsity and the non-negativity constraint in the original SLIM-objective with sub-sampling experiments that simulate scenarios of cold-starting and large catalog sizes compared to relatively small user base, which often occur in practice.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122385755","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: 25

Overlapping Community Detection in Static and Dynamic Networks 静态和动态网络中的重叠社区检测

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3372185

Renny Márquez

引用次数: 5

MRAEA

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371804

Xin Mao, Wenting Wang, Huimin Xu, Man Lan, Yuanbin Wu

{"title":"MRAEA","authors":"Xin Mao, Wenting Wang, Huimin Xu, Man Lan, Yuanbin Wu","doi":"10.1145/3336191.3371804","DOIUrl":"https://doi.org/10.1145/3336191.3371804","url":null,"abstract":"Entity alignment to find equivalent entities in cross-lingual Knowledge Graphs (KGs) plays a vital role in automatically integrating multiple KGs. Existing translation-based entity alignment methods jointly model the cross-lingual knowledge and monolingual knowledge into one unified optimization problem. On the other hand, the Graph Neural Network (GNN) based methods either ignore the node differentiations, or represent relation through entity or triple instances. They all fail to model the meta semantics embedded in relation nor complex relations such as n-to-n and multi-graphs. To tackle these challenges, we propose a novel Meta Relation Aware Entity Alignment (MRAEA) to directly model cross-lingual entity embeddings by attending over the node's incoming and outgoing neighbors and its connected relations' meta semantics. In addition, we also propose a simple and effective bi-directional iterative strategy to add new aligned seeds during training. Our experiments on all three benchmark entity alignment datasets show that our approach consistently outperforms the state-of-the-art methods, exceeding by 15%-58% on Hit@1. Through an extensive ablation study, we validate that the proposed meta relation aware representations, relation aware self-attention and bi-directional iterative strategy of new seed selection all make contributions to significant performance improvement. The code is available at https://github.com/MaoXinn/MRAEA.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122275021","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

Hierarchical User Profiling for E-commerce Recommender Systems 电子商务推荐系统的分层用户分析

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371827

Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Dawei Yin

{"title":"Hierarchical User Profiling for E-commerce Recommender Systems","authors":"Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Dawei Yin","doi":"10.1145/3336191.3371827","DOIUrl":"https://doi.org/10.1145/3336191.3371827","url":null,"abstract":"Hierarchical user profiling that aims to model users' real-time interests in different granularity is an essential issue for personalized recommendations in E-commerce. On one hand, items (i.e. products) are usually organized hierarchically in categories, and correspondingly users' interests are naturally hierarchical on different granularity of items and categories. On the other hand, multiple granularity oriented recommendations become very popular in E-commerce sites, which require hierarchical user profiling in different granularity as well. In this paper, we propose HUP, a Hierarchical User Profiling framework to solve the hierarchical user profiling problem in E-commerce recommender systems. In HUP, we provide a Pyramid Recurrent Neural Networks, equipped with Behavior-LSTM to formulate users' hierarchical real-time interests at multiple scales. Furthermore, instead of simply utilizing users' item-level behaviors (e.g., ratings or clicks) in conventional methods, HUP harvests the sequential information of users' temporal finely-granular interactions (micro-behaviors, e.g., clicks on components of items like pictures or comments, browses with navigation of the search engines or recommendations) for modeling. Extensive experiments on two real-world E-commerce datasets demonstrate the significant performance gains of the HUP against state-of-the-art methods for the hierarchical user profiling and recommendation problems. We release the codes and datasets at https://github.com/guyulongcs/WSDM2020_HUP.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126574228","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: 65

Entities with Quantities: Extraction, Search, and Ranking 有数量的实体:抽取、搜索和排序

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371860

Vinh Thinh Ho, K. Pal, Niko Kleer, K. Berberich, G. Weikum

引用次数: 10

SPread: Automated Financial Metric Extraction and Spreading Tool from Earnings Reports 从收益报告中自动提取和传播财务指标工具

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371869

Armineh Nourbakhsh, M. Ghassemi, Steven Pomerville

引用次数: 4

Workshop on Privacy in NLP (PrivateNLP 2020) NLP私隐工作坊(PrivateNLP 2020)

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371881

Oluwaseyi Feyisetan, S. Ghanavati, Patricia Thaine

引用次数: 7

Predicting Human Mobility via Attentive Convolutional Network 通过细心卷积网络预测人类移动性

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371846

Congcong Miao, Ziyan Luo, Fengzhu Zeng, Jilong Wang

{"title":"Predicting Human Mobility via Attentive Convolutional Network","authors":"Congcong Miao, Ziyan Luo, Fengzhu Zeng, Jilong Wang","doi":"10.1145/3336191.3371846","DOIUrl":"https://doi.org/10.1145/3336191.3371846","url":null,"abstract":"Predicting human mobility is an important trajectory mining task for various applications, ranging from smart city planning to personalized recommendation system. While most of previous works adopt GPS tracking data to model human mobility, the recent fast-growing geo-tagged social media (GTSM) data brings new opportunities to this task. However, predicting human mobility on GTSM data is not trivial because of three challenges: 1) extreme data sparsity; 2) high order sequential patterns of human mobility and 3) evolving preference of users for tagging. In this paper, we propose ACN, an attentive convolutional network model for predicting human mobility from sparse and complex GTSM data. In ACN, we firstly design a multi-dimension embedding layer which jointly embeds key features (i.e., spatial, temporal and user features) that govern human mobility. Then, we regard the embedded trajectory as an \"image\" and learn short-term sequential patterns as local features of the image using convolution filters. Instead of directly using convention filters, we design hybrid dilated and separable convolution filters to effectively capture high order sequential patterns from lengthy trajectory. In addition, we propose an attention mechanism which learns the user long-term preference to augment convolutional network for mobility prediction. We conduct extensive experiments on three publicly available GTSM datasets to evaluate the effectiveness of our model. The results demonstrate that ACN consistently outperforms existing state-of-art mobility prediction approaches on a variety of common evaluation metrics.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125273952","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: 13