Proceedings of the 13th ACM Conference on Recommender Systems最新文献_第7页

Guiding creative design in online advertising 指导网络广告创意设计

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347022

Shaunak Mishra, Manisha Verma, J. Gligorijevic

{"title":"Guiding creative design in online advertising","authors":"Shaunak Mishra, Manisha Verma, J. Gligorijevic","doi":"10.1145/3298689.3347022","DOIUrl":"https://doi.org/10.1145/3298689.3347022","url":null,"abstract":"Ad creatives (text and images) for a brand play an influential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers) typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspiration from prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand's ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the efficacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129435913","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: 11

AnnoMathTeX - a formula identifier annotation recommender system for STEM documents 一个用于STEM文档的公式标识符注释推荐系统

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347042

Philipp Scharpf, Ian Mackerracher, M. Schubotz, J. Beel, Corinna Breitinger, Bela Gipp

引用次数: 21

RecSys '19 joint workshop on interfaces and human decision making for recommender systems RecSys第19届关于推荐系统的界面和人类决策的联合研讨会

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346971

Peter Brusilovsky, M. Degemmis, A. Felfernig, P. Lops, J. O'Donovan, G. Semeraro, M. Willemsen

引用次数: 0

Deep generative ranking for personalized recommendation 个性化推荐的深度生成排序

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347012

Huafeng Liu, Jingxuan Wen, L. Jing, Jian Yu

{"title":"Deep generative ranking for personalized recommendation","authors":"Huafeng Liu, Jingxuan Wen, L. Jing, Jian Yu","doi":"10.1145/3298689.3347012","DOIUrl":"https://doi.org/10.1145/3298689.3347012","url":null,"abstract":"Recommender systems offer critical services in the age of mass information. Personalized ranking has been attractive both for content providers and customers due to its ability of creating a user-specific ranking on the item set. Although the powerful factor-analysis methods including latent factor models and deep neural network models have achieved promising results, they still suffer from the challenging issues, such as sparsity of recommendation data, uncertainty of optimization, and etc. To enhance the accuracy and generalization of recommender system, in this paper, we propose a deep generative ranking (DGR) model under the Wasserstein autoencoder framework. Specifically, DGR simultaneously generates the pointwise implicit feedback data (via a Beta-Bernoulli distribution) and creates the pairwise ranking list by sufficient exploiting both interacted and non-interacted items for each user. DGR can be efficiently inferred by minimizing its penalized evidence lower bound. Meanwhile, we theoretically analyze the generalization error bounds of DGR model to guarantee its performance in extremely sparse feedback data. A series of experiments on four large-scale datasets (Movielens (20M), Netflix, Epinions and Yelp in movie, product and business domains) have been conducted. By comparing with the state-of-the-art methods, the experimental results demonstrate that DGR consistently benefit the recommendation system in ranking estimation task, especially for the near-cold-start-users (with less than five interacted items).","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133471370","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

Personalized fairness-aware re-ranking for microlending 对小额贷款的个性化公平重新排序

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347016

Weiwen Liu, J. Guo, Nasim Sonboli, R. Burke, Shengyu Zhang

{"title":"Personalized fairness-aware re-ranking for microlending","authors":"Weiwen Liu, J. Guo, Nasim Sonboli, R. Burke, Shengyu Zhang","doi":"10.1145/3298689.3347016","DOIUrl":"https://doi.org/10.1145/3298689.3347016","url":null,"abstract":"Microlending can lead to improved access to capital in impoverished countries. Recommender systems could be used in microlending to provide efficient and personalized service to lenders. However, increasing concerns about discrimination in machine learning hinder the application of recommender systems to the microfinance industry. Most previous recommender systems focus on pure personalization, with fairness issue largely ignored. A desirable fairness property in microlending is to give borrowers from different demographic groups a fair chance of being recommended, as stated by Kiva. To achieve this goal, we propose a Fairness-Aware Re-ranking (FAR) algorithm to balance ranking quality and borrower-side fairness. Furthermore, we take into consideration that lenders may differ in their receptivity to the diversification of recommended loans, and develop a Personalized Fairness-Aware Re-ranking (PFAR) algorithm. Experiments on a real-world dataset from Kiva.org show that our re-ranking algorithm can significantly promote fairness with little sacrifice in accuracy, and be attentive to individual lender preference on loan diversity.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643043","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: 56

Predicting online performance of job recommender systems with offline evaluation 用离线评价预测工作推荐系统的在线性能

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347032

Adrien Mogenet, T. Pham, Masahiro Kazama, Jiali Kong

引用次数: 5

SMORe 提供

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1093/nq/s8-v.118.257i

Chih‐Ming Chen, Ting-Hsiang Wang, Chuan-Ju Wang, Ming-Feng Tsai

引用次数: 1

A simple multi-armed nearest-neighbor bandit for interactive recommendation 一个简单的多臂最近邻强盗交互式推荐

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3347040

Javier Sanz-Cruzado, P. Castells, Esther López

引用次数: 28

Sampling-bias-corrected neural modeling for large corpus item recommendations 大型语料库项目推荐的抽样偏差校正神经模型

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346996

Xinyang Yi, Ji Yang, Lichan Hong, D. Cheng, L. Heldt, A. Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi

{"title":"Sampling-bias-corrected neural modeling for large corpus item recommendations","authors":"Xinyang Yi, Ji Yang, Lichan Hong, D. Cheng, L. Heldt, A. Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi","doi":"10.1145/3298689.3346996","DOIUrl":"https://doi.org/10.1145/3298689.3346996","url":null,"abstract":"Many recommendation systems retrieve and score items from a very large corpus. A common recipe to handle data sparsity and power-law item distribution is to learn item representations from its content features. Apart from many content-aware systems based on matrix factorization, we consider a modeling framework using two-tower neural net, with one of the towers (item tower) encoding a wide variety of item content features. A general recipe of training such two-tower models is to optimize loss functions calculated from in-batch negatives, which are items sampled from a random mini-batch. However, in-batch loss is subject to sampling biases, potentially hurting model performance, particularly in the case of highly skewed distribution. In this paper, we present a novel algorithm for estimating item frequency from streaming data. Through theoretical analysis and simulation, we show that the proposed algorithm can work without requiring fixed item vocabulary, and is capable of producing unbiased estimation and being adaptive to item distribution change. We then apply the sampling-bias-corrected modeling approach to build a large scale neural retrieval system for YouTube recommendations. The system is deployed to retrieve personalized suggestions from a corpus with tens of millions of videos. We demonstrate the effectiveness of sampling-bias correction through offline experiments on two real-world datasets. We also conduct live A/B testings to show that the neural retrieval system leads to improved recommendation quality for YouTube.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120957567","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: 148

User-centered evaluation of strategies for recommending sequences of points of interest to groups 以用户为中心的评价策略，为群体推荐兴趣点序列

Proceedings of the 13th ACM Conference on Recommender Systems Pub Date : 2019-09-10 DOI: 10.1145/3298689.3346988

Daniel Herzog, W. Wörndl

引用次数: 11