Proceedings of the 10th ACM Conference on Recommender Systems最新文献

Opening Remarks 开场白

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-15 DOI: 10.1145/2959100.3057279

Shilad Sen, Werner Geyer, J. Freyne, P. Castells

引用次数: 0

Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization 基于相似用户和项正则化的异步分布式矩阵分解

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959161

Bikash Joshi, F. Iutzeler, Massih-Reza Amini

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引用次数: 8

Tutorial: Lessons Learned from Building Real-life Recommender Systems 教程:构建现实生活中的推荐系统的经验教训

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959194

X. Amatriain, D. Agarwal

{"title":"Tutorial: Lessons Learned from Building Real-life Recommender Systems","authors":"X. Amatriain, D. Agarwal","doi":"10.1145/2959100.2959194","DOIUrl":"https://doi.org/10.1145/2959100.2959194","url":null,"abstract":"In 2006, Netflix announced a $1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community, it put the focus on the wrong approach and metric while leaving many important factors out. In this tutorial we will describe the advances in Recommender Systems in the last 10 years from an industry perspective based on the instructors' personal experience at companies like Quora, LinkedIn, Netflix, or Yahoo! We will do so in the form of different lessons learned through the years. Some of those lessons will describe the different components of modern recommender systems such as: personalized ranking, similarity, explanations, context-awareness, or multi-armed bandits. Others will also review the usage of novel algorithmic approaches such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or Listwise Learning-to-rank. Others will dive into details of the importance of gathering the right data or using the correct optimization metric. But, most importantly, we will give many examples of prototypical industrial-scale recommender systems with special focus on those unsolved challenges that should define the future of the recommender systems area.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116512613","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

Bayesian Low-Rank Determinantal Point Processes 贝叶斯低秩行列式点过程

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959178

Mike Gartrell, U. Paquet, Noam Koenigstein

{"title":"Bayesian Low-Rank Determinantal Point Processes","authors":"Mike Gartrell, U. Paquet, Noam Koenigstein","doi":"10.1145/2959100.2959178","DOIUrl":"https://doi.org/10.1145/2959100.2959178","url":null,"abstract":"Determinantal point processes (DPPs) are an emerging model for encoding probabilities over subsets, such as shopping baskets, selected from a ground set, such as an item catalog. They have recently proved to be appealing models for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Prior work has shown that using a low-rank factorization of this kernel provides scalability improvements that open the door to training on large-scale datasets and computing online recommendations, both of which are infeasible with standard DPP models that use a full-rank kernel. A low-rank DPP model can be trained using an optimization-based method, such as stochastic gradient ascent, to find a point estimate of the kernel parameters, which can be performed efficiently on large-scale datasets. However, this approach requires careful tuning of regularization parameters to prevent overfitting and provide good predictive performance, which can be computationally expensive. In this paper we present a Bayesian method for learning a low-rank factorization of this kernel, which provides automatic control of regularization. We show that our Bayesian low-rank DPP model can be trained efficiently using stochastic gradient Hamiltonian Monte Carlo (SGHMC). Our Bayesian model generally provides better predictive performance on several real-world product recommendation datasets than optimization-based low-rank DPP models trained using stochastic gradient ascent, and better performance than several state-of-the art recommendation methods in many cases.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908101","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: 59

Local Item-Item Models For Top-N Recommendation Top-N推荐的局部项目-项目模型

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959185

Evangelia Christakopoulou, G. Karypis

引用次数: 115

Recommending the World's Knowledge: Application of Recommender Systems at Quora 推荐世界知识:Quora推荐系统的应用

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959128

Lei Yang, X. Amatriain

引用次数: 15

News Recommendations at scale at Bloomberg Media: Challenges and Approaches 彭博媒体的大规模新闻推荐:挑战和方法

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959118

Dhaval Shah, Pramod Koneru, Parth Shah, Rohit Parimi

{"title":"News Recommendations at scale at Bloomberg Media: Challenges and Approaches","authors":"Dhaval Shah, Pramod Koneru, Parth Shah, Rohit Parimi","doi":"10.1145/2959100.2959118","DOIUrl":"https://doi.org/10.1145/2959100.2959118","url":null,"abstract":"In the past decade, news consumption through traditional channels such as print has been on the decline while online and digital news consumption has been steadily growing. Bloomberg, renowned for its products in the financial world, has a very strong presence in the news and media industry. Bloomberg Media, on an average, publishes 400-500 stories and videos per day and we have close to 30 million unique visitors on our websites and mobile applications every month consuming this content. At such a scale it is very important to recommend relevant information for a good user experience. Recommendations in the News and Media domain bring a unique set of challenges due to the dynamic nature of the data as well as unique consumption patterns. The biggest challenge with building recommendation systems in the News domain is the dynamic nature of the domain itself; new content is published every few minutes and majority of the content has a short shelf life, i.e., the news is not relevant to users after a certain time span and the time span is generally of the order of hours rather than days, making it important to deliver relevant content in a timely manner. Moreover, our users consume content differently based on time of day. For example, some users whose focus is market news and market data during the day consume more long form and generic articles and videos in the evening. User preferences, along with being cyclical in nature, tend to change over time, so algorithms need to adapt to the changing taste of the user. In addition, we need to ensure that the users do get their share of important/trending news and are not put into a filter bubble. In this talk, we will present some novel techniques we have applied to popular approaches in the field of Recommender Systems to be able to address the unique challenges which the news domain presents.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694154","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

RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS) RecSys'16深度学习推荐系统(DLRS)研讨会

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959202

Alexandros Karatzoglou, Balázs Hidasi, D. Tikk, Oren Sar Shalom, Haggai Roitman, Bracha Shapira, L. Rokach

引用次数: 19

When Recommendation Systems Go Bad 当推荐系统出现问题时

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959117

Evan Estola

引用次数: 5

HCI for Recommender Systems: the Past, the Present and the Future 推荐系统的人机交互：过去、现在和未来

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959158

André Calero Valdez, M. Ziefle, K. Verbert

引用次数: 44