Proceedings of the 13th International Conference on Web Search and Data Mining最新文献_第2页

Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce 为情人节购物的时间:电子商务中的购物场合和顺序推荐

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371836

Jianling Wang, Raphael Louca, D. Hu, Caitlin Cellier, James Caverlee, Liangjie Hong

{"title":"Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce","authors":"Jianling Wang, Raphael Louca, D. Hu, Caitlin Cellier, James Caverlee, Liangjie Hong","doi":"10.1145/3336191.3371836","DOIUrl":"https://doi.org/10.1145/3336191.3371836","url":null,"abstract":"Currently, most sequence-based recommendation models aim to predict a user's next actions (e.g. next purchase) based on their past actions. These models either capture users' intrinsic preference (e.g. a comedy lover, or a fan of fantasy) from their long-term behavior patterns or infer their current needs by emphasizing recent actions. However, in e-commerce, intrinsic user behavior may be shifted by occasions such as birthdays, anniversaries, or gifting celebrations (Valentine's Day or Mother's Day), leading to purchases that deviate from long-term preferences and are not related to recent actions. In this work, we propose a novel next-item recommendation system which models a user's default, intrinsic preference, as well as two different kinds of occasion-based signals that may cause users to deviate from their normal behavior. More specifically, this model is novel in that it: (1) captures a personal occasion signal using an attention layer that models reoccurring occasions specific to that user (e.g. a birthday); (2) captures a global occasion signal using an attention layer that models seasonal or reoccurring occasions for many users (e.g. Christmas); (3) balances the user's intrinsic preferences with the personal and global occasion signals for different users at different timestamps with a gating layer. We explore two real-world e-commerce datasets (Amazon and Etsy) and show that the proposed model outperforms state-of-the-art models by 7.62% and 6.06% in predicting users' next purchase.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"4 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":"129014895","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: 24

Think like a Human: Constructing Cognitive-oriented Retrieval Model for Web Search 像人一样思考:构建面向认知的网络搜索检索模型

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3372180

Xiangsheng Li

引用次数: 0

OpenNIR: A Complete Neural Ad-Hoc Ranking Pipeline OpenNIR:一个完整的神经自组织排序管道

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371864

Sean MacAvaney

{"title":"OpenNIR: A Complete Neural Ad-Hoc Ranking Pipeline","authors":"Sean MacAvaney","doi":"10.1145/3336191.3371864","DOIUrl":"https://doi.org/10.1145/3336191.3371864","url":null,"abstract":"With the growing popularity of neural approaches for ad-hoc ranking, there is a need for tools that can effectively reproduce prior results and ease continued research by supporting current state-of-the-art approaches. Although several excellent neural ranking tools exist, none offer an easy end-to-end ad-hoc neural raking pipeline. A complete pipeline is particularly important for ad-hoc ranking because there are numerous parameter settings that have a considerable effect on the ultimate performance yet often are under-reported in current work (e.g., initial ranking settings, re-ranking threshold, training sampling strategy, etc.). In this work, I present a complete ad-hoc neural ranking pipeline which addresses these shortcomings: OpenNIR. The pipeline is easy to use (a single command will download required data, train, and evaluate a model), yet highly configurable, allowing for continued work in areas that are understudied. Aside from the core pipeline, the software also includes several bells and whistles that make use of components of the pipeline, such as performance benchmarking and tuning of unsupervised ranker parameters for fair comparisons against traditional baselines. The pipeline and these capabilities are demonstrated. The code is available, and contributions are welcome.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"72 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":"126528571","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: 38

Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments 在线控制实验结果评估中的挑战、最佳实践和陷阱

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371871

Somit Gupta, Xiaolin Shi, Pavel A. Dmitriev, Xin Fu, Avijit Mukherjee

{"title":"Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments","authors":"Somit Gupta, Xiaolin Shi, Pavel A. Dmitriev, Xin Fu, Avijit Mukherjee","doi":"10.1145/3336191.3371871","DOIUrl":"https://doi.org/10.1145/3336191.3371871","url":null,"abstract":"A/B Testing is the gold standard to estimate the causal relationship between a change in a product and its impact on key outcome measures. It is widely used in the industry to test changes ranging from simple copy change or UI change to more complex changes like using machine learning models to personalize user experience. The key aspect of A/B testing is evaluation of experiment results. Designing the right set of metrics - correct outcome measures, data quality indicators, guardrails that prevent harm to business, and a comprehensive set of supporting metrics to understand the \"why\" behind the key movements is the #1 challenge practitioners face when trying to scale their experimentation program [11, 14]. On the technical side, improving sensitivity of experiment metrics is a hard problem and an active research area, with large practical implications as more and more small and medium size businesses are trying to adopt A/B testing and suffer from insufficient power. In this tutorial we will discuss challenges, best practices, and pitfalls in evaluating experiment results, focusing on both lessons learned and practical guidelines as well as open research questions. A version of this tutorial was also present at KDD 2019 [23]. It was attended by around 150 participants.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"110 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":"134433527","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

Temporal Pattern of Retweet(s) Help to Maximize Information Diffusion in Twitter 推文的时间模式有助于推特信息传播的最大化

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3372181

Ayan Kumar Bhowmick

{"title":"Temporal Pattern of Retweet(s) Help to Maximize Information Diffusion in Twitter","authors":"Ayan Kumar Bhowmick","doi":"10.1145/3336191.3372181","DOIUrl":"https://doi.org/10.1145/3336191.3372181","url":null,"abstract":"Twitter is currently a popular microblogging platform for spread of information by users in the form of tweet messages. Such tweets are shared with followers of the seed user who may reshare it with their own set of followers. Long chain of such retweets form cascades. For effective diffusion of information through such Twitter cascades, we identify two different objectives based on using temporal sequence of retweets. Firstly, we aim to infer the structure of influence trees of Twitter cascades, denoting the who-influenced-whom relationship among retweeting users in the cascade, that can play a significant role in identifying critical paths in the network for information dissemination. The constructed trees closely resemble ground truth influence trees of empirical cascades with high retweet count. Secondly, we propose a fast and efficient algorithm for detection of influential users by identifying anchor nodes from temporal retweet sequence. Identification of such a diverse set of influential users enable a faster diffusion of tweets to a large and diverse population, when targeted as seeds thereby maximizing the influence spread, facilitating several applications including viral marketing, disease control and news dissemination.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"18 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":"114685736","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

Metrics, User Models, and Satisfaction 指标、用户模型和满意度

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371799

A. Wicaksono, Alistair Moffat

引用次数: 19

Enhancing Re-finding Behavior with External Memories for Personalized Search 利用外部记忆增强个性化搜索的重新发现行为

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371794

Yujia Zhou, Zhicheng Dou, Ji-rong Wen

引用次数: 19

FAQAugmenter: Suggesting Questions for Enterprise FAQ Pages faqaugmentor:为企业FAQ页面提出问题建议

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371862

Ankush Chatterjee, Manish Gupta, Puneet Agrawal

引用次数: 5

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems 评估-行动-反思:对话系统和推荐系统之间的深度互动

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371769

Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua

{"title":"Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems","authors":"Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua","doi":"10.1145/3336191.3371769","DOIUrl":"https://doi.org/10.1145/3336191.3371769","url":null,"abstract":"Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation\"Action\" Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"17 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":"123673216","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: 183

ENTYFI

Proceedings of the 13th International Conference on Web Search and Data Mining Pub Date : 2020-01-20 DOI: 10.1145/3336191.3371808

C. Chu, Simon Razniewski, G. Weikum

引用次数: 1