Proceedings of the 10th ACM Conference on Recommender Systems最新文献_第9页

Field-aware Factorization Machines for CTR Prediction CTR预测的现场感知分解机

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959134

Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin

引用次数: 609

Marsbot: Building a Personal Assistant 玛氏:打造个人助理

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959119

Max Sklar

引用次数: 1

The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems 情境转向:从情境感知到情境驱动的推荐系统

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959136

Roberto Pagano, P. Cremonesi, M. Larson, Balázs Hidasi, D. Tikk, Alexandros Karatzoglou, Massimo Quadrana

引用次数: 41

Multi-Word Generative Query Recommendation Using Topic Modeling 基于主题建模的多词生成查询推荐

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959154

M. Mitsui, C. Shah

引用次数: 6

Efficient Bayesian Methods for Graph-based Recommendation 基于图的高效贝叶斯推荐方法

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959132

Ramon Lopes, R. Assunção, Rodrygo L. T. Santos

引用次数: 18

Query-based Music Recommendations via Preference Embedding 基于偏好嵌入的查询音乐推荐

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959169

Chih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang

{"title":"Query-based Music Recommendations via Preference Embedding","authors":"Chih-Ming Chen, Ming-Feng Tsai, Yu-Ching Lin, Yi-Hsuan Yang","doi":"10.1145/2959100.2959169","DOIUrl":"https://doi.org/10.1145/2959100.2959169","url":null,"abstract":"A common scenario considered in recommender systems is to predict a user's preferences on unseen items based on his/her preferences on observed items. A major limitation of this scenario is that a user might be interested in different things each time when using the system, but there is no way to allow the user to actively alter or adjust the recommended results. To address this issue, we propose the idea of \"query-based recommendation\" that allows a user to specify his/her search intention while exploring new items, thereby incorporating the concept of information retrieval into recommendation systems. Moreover, the idea is more desirable when the user intention can be expressed in different ways. Take music recommendation as an example: the proposed system allows a user to explore new song tracks by specifying either a track, an album, or an artist. To enable such heterogeneous queries in a recommender system, we present a novel technique called \"Heterogeneous Preference Embedding\" to encode user preference and query intention into low-dimensional vector spaces. Then, with simple search methods or similarity calculations, we can use the encoded representation of queries to generate recommendations. This method is fairly flexible and it is easy to add other types of information when available. Evaluations on three music listening datasets confirm the effectiveness of the proposed method over the state-of-the-art matrix factorization and network embedding methods.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 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":"129003081","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

RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems RecSys'16推荐系统界面与人工决策联合研讨会

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959199

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

引用次数: 1

Hypothesis Testing: How to Eliminate Ideas as Soon as Possible 假设检验:如何尽快消除想法

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959127

Roman Zykov

{"title":"Hypothesis Testing: How to Eliminate Ideas as Soon as Possible","authors":"Roman Zykov","doi":"10.1145/2959100.2959127","DOIUrl":"https://doi.org/10.1145/2959100.2959127","url":null,"abstract":"Retail Rocket helps web shoppers make better shopping decisions by providing personalized real-time recommendations through multiple channels with over 100MM unique monthly users and 1000+ retail partners. The rapid improvement of the product is important to win on the high-concurrency market of real-time personalization platforms. The necessity of introducing constant innovations and improvements of algorithms for recommendation systems requires correct tools and a process of rapid testing of hypotheses. It's not a secret that 9 out of 10 hypotheses actually do not improve the performance at least. We had the task stated as follows: How to detect and eliminate the idea that doesn't improve as early as possible, to spend a minimum of resources on that process. In the report we will talk about: How we make our process of hypotheses testing faster. One programming language for R&D. Enmity and friendship of offline and online metrics. Why it is difficult to predict the impact of changing diversity of algorithms. What is the benefit of AA/BB online tests. Bayesian statistics for the evaluation of online tests. Roman Zykov is the Chief Data Scientist at the Retail Rocket. In Retail Rocket is responsible for algorithms of personalized and non-personalized recommendations. Previous to Retail Rocket, Roman was the Head of analytics at the biggest e-commerce companies for almost ten years. He received Ms.Sc. in applied mathematics and physics from the MIPhT in 2004.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 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":"129501825","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: 0

Algorithms Aside: Recommendation As The Lens Of Life 抛开算法:作为生活镜头的推荐

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959164

Tamas Motajcsek, J. Moine, M. Larson, Daniel Kohlsdorf, A. Lommatzsch, D. Tikk, Omar Alonso, P. Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, F. Garzotto, A. Göker, F. Hopfgartner, D. Malagoli, T. Nguyen, J. Novak, F. Ricci, M. Scriminaci, M. Tkalcic, Anna Zacchi

{"title":"Algorithms Aside: Recommendation As The Lens Of Life","authors":"Tamas Motajcsek, J. Moine, M. Larson, Daniel Kohlsdorf, A. Lommatzsch, D. Tikk, Omar Alonso, P. Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, F. Garzotto, A. Göker, F. Hopfgartner, D. Malagoli, T. Nguyen, J. Novak, F. Ricci, M. Scriminaci, M. Tkalcic, Anna Zacchi","doi":"10.1145/2959100.2959164","DOIUrl":"https://doi.org/10.1145/2959100.2959164","url":null,"abstract":"In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"76 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":"125876119","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: 6

LSRS'16: Workshop on Large-Scale Recommender Systems lrs '16:大型推荐系统研讨会

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959206

Tao Ye, Danny Bickson, Denis Parra

引用次数: 0