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Proceedings of the 9th ACM Conference on Recommender Systems最新文献

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Interactive Recommender Systems: Tutorial 互动推荐系统:教程
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2792840
H. Steck, R. V. Zwol, Chris Johnson
{"title":"Interactive Recommender Systems: Tutorial","authors":"H. Steck, R. V. Zwol, Chris Johnson","doi":"10.1145/2792838.2792840","DOIUrl":"https://doi.org/10.1145/2792838.2792840","url":null,"abstract":"In this tutorial we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In this tutorial, we outline the various aspects that are crucial for a smooth and effective user experience. In particular, we present our insights from several A/B tests. The tutorial will help researchers and practitioners in the RecSys community to gain a deeper understanding of the challenges related to the application of recommender systems in the online video and music entertainment business.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130836404","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
Automated Recommendation of Healthy, Personalised Meal Plans 自动推荐健康、个性化的膳食计划
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796551
Morgan Harvey, David Elsweiler
{"title":"Automated Recommendation of Healthy, Personalised Meal Plans","authors":"Morgan Harvey, David Elsweiler","doi":"10.1145/2792838.2796551","DOIUrl":"https://doi.org/10.1145/2792838.2796551","url":null,"abstract":"Poor health due to a lack of understanding of nutrition is a major problem in the modern world, one which could potentially be addressed via the use of recommender systems. In this demo we present a system to generate meal plans for users which they will not only like, based on their taste preferences, but will also conform to daily nutritional guidelines. The interface allows the selection of recipes for breakfast, lunch and dinner and can automatically complete a daily meal plan or can generate entire plans itself.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131718245","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: 22
2nd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2015) 第二届电视与网络视频推荐系统研讨会(RecSysTV 2015)
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798717
Jan Neumann, Danny Bickson, H. Sayyadi, R. Turrin, John Hannon
{"title":"2nd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2015)","authors":"Jan Neumann, Danny Bickson, H. Sayyadi, R. Turrin, John Hannon","doi":"10.1145/2792838.2798717","DOIUrl":"https://doi.org/10.1145/2792838.2798717","url":null,"abstract":"For many households the television is the central entertainment hub in their home, and the average TV viewer spends about half of their leisure time in front of a TV. At any given moment, a costumer has hundreds to thousands of entertainment choices available, which makes some sort of automatic, personalized recommendations desirable to help consumers deal with the often overwhelming number of choices they face. The 2nd Workshop on Recommendation Systems for Television and Online Video aims to offer a place to present and discuss the latest academic and industrial research on recommendation systems for this challenging and exciting application domain","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123835731","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
Incremental Matrix Factorization via Feature Space Re-learning for Recommender System 基于特征空间再学习的增量矩阵分解推荐系统
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799668
Qiang Song, Jian Cheng, Hanqing Lu
{"title":"Incremental Matrix Factorization via Feature Space Re-learning for Recommender System","authors":"Qiang Song, Jian Cheng, Hanqing Lu","doi":"10.1145/2792838.2799668","DOIUrl":"https://doi.org/10.1145/2792838.2799668","url":null,"abstract":"Matrix factorization is widely used in Recommender Systems. Although existing popular incremental matrix factorization methods are effectively in reducing time complexity, they simply assume that the similarity between items or users is invariant. For instance, they keep the item feature matrix unchanged and just update the user matrix without re-training the entire model. However, with the new users growing continuously, the fitting error would be accumulated since the extra distribution information of items has not been utilized. In this paper, we present an alternative and reasonable approach, with a relaxed assumption that the similarity between items (users) is relatively stable after updating. Concretely, utilizing the prediction error of the new data as the auxiliary features, our method updates both feature matrices simultaneously, and thus users' preference can be better modeled than merely adjusting one corresponded feature matrix. Besides, our method maintains the feature dimension in a smaller size through taking advantage of matrix sketching. Experimental results show that our proposal outperforms the existing incremental matrix factorization methods.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1981 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113966644","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: 20
Gaussian Ranking by Matrix Factorization 基于矩阵分解的高斯排序
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800185
H. Steck
{"title":"Gaussian Ranking by Matrix Factorization","authors":"H. Steck","doi":"10.1145/2792838.2800185","DOIUrl":"https://doi.org/10.1145/2792838.2800185","url":null,"abstract":"The ranking quality at the top of the list is crucial in many real-world applications of recommender systems. In this paper, we present a novel framework that allows for pointwise as well as listwise training with respect to various ranking metrics. This is based on a training objective function where we assume that, for given a user, the recommender system predicts scores for all items that follow approximately a Gaussian distribution. We motivate this assumption from the properties of implicit feedback data. As a model, we use matrix factorization and extend it by non-linear activation functions, as customary in the literature of artificial neural networks. In particular, we use non-linear activation functions derived from our Gaussian assumption. Our preliminary experimental results show that this approach is competitive with state-of-the-art methods with respect to optimizing the Area under the ROC curve, while it is particularly effective in optimizing the head of the ranked list.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114622066","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: 28
Event Recommendation using Twitter Activity 使用Twitter活动进行事件推荐
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796556
Axel Magnuson, V. Dialani, Deepa Mallela
{"title":"Event Recommendation using Twitter Activity","authors":"Axel Magnuson, V. Dialani, Deepa Mallela","doi":"10.1145/2792838.2796556","DOIUrl":"https://doi.org/10.1145/2792838.2796556","url":null,"abstract":"User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116617777","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: 12
Recommendations for Live TV 电视直播建议
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799494
Jan Neumann, H. Sayyadi
{"title":"Recommendations for Live TV","authors":"Jan Neumann, H. Sayyadi","doi":"10.1145/2792838.2799494","DOIUrl":"https://doi.org/10.1145/2792838.2799494","url":null,"abstract":"Despite the rise in video-on-demand consumption, live TV is still the most popular way to consume video entertainment. At Comcast we are developing novel ways to make it easy for our customers to access the live TV content that is interesting and relevant for them at the current moment. In this talk, we will describe some of the latest research at Comcast Labs on learning the favorite stations and programs for a customer at a given time of day, personalizing their TV guide, and informing our customers of what is trending on TV and social media at that moment, so that they can participate in the shared experience of live TV. We will explain how usage data is processed using both batch and real-time approaches to personalize the experience for Comcast's customers","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127851076","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
Nudging Grocery Shoppers to Make Healthier Choices 促使购物者做出更健康的选择
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799669
Elizabeth Wayman, S. Madhvanath
{"title":"Nudging Grocery Shoppers to Make Healthier Choices","authors":"Elizabeth Wayman, S. Madhvanath","doi":"10.1145/2792838.2799669","DOIUrl":"https://doi.org/10.1145/2792838.2799669","url":null,"abstract":"Despite the rampant increase in obesity rates and concomitant increases in rates of mortality from heart disease, cancer and diabetes, getting the general public to adopt a healthy diet has proven to be challenging for a variety of reasons. In this paper, we describe Foodle, a research project aimed at providing automated, personalized and goal-driven dietary guidance to users based on their grocery receipt data, by leveraging the availability of digital receipts for grocery store purchases. We discuss challenges faced, the current state of the project, and directions for future work.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126276680","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: 9
LSRS'15: Workshop on Large-Scale Recommender Systems lrs '15:大型推荐系统研讨会
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798715
Tao Ye, Danny Bickson, N. Ampazis, A. Benczúr
{"title":"LSRS'15: Workshop on Large-Scale Recommender Systems","authors":"Tao Ye, Danny Bickson, N. Ampazis, A. Benczúr","doi":"10.1145/2792838.2798715","DOIUrl":"https://doi.org/10.1145/2792838.2798715","url":null,"abstract":"With the increase of data collected and computation power available, modern recommender systems are ever facing new challenges. While complex models are developed in academia, industry practice seems to focus on relatively simple techniques that can deal with the magnitude of data and the need to distribute the computation. The workshop on large-scale recommender systems (LSRS) is a meeting place for industry and academia to discuss the current and future challenges of applied large-scale recommender systems.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131420771","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
Recommending Fair Payments for Large-Scale Social Ridesharing 建议公平支付大规模社会拼车
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800177
Filippo Bistaffa, Alessandro Filippo, G. Chalkiadakis, S. Ramchurn
{"title":"Recommending Fair Payments for Large-Scale Social Ridesharing","authors":"Filippo Bistaffa, Alessandro Filippo, G. Chalkiadakis, S. Ramchurn","doi":"10.1145/2792838.2800177","DOIUrl":"https://doi.org/10.1145/2792838.2800177","url":null,"abstract":"We perform recommendations for the Social Ridesharing scenario, in which a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on how much one should pay for taking a ride with friends. More formally, we propose the first approach that can compute fair coalitional payments that are also stable according to the game-theoretic concept of the kernel for systems with thousands of agents in real-world scenarios. Our tests, based on real datasets for both spatial (GeoLife) and social data (Twitter), show that our approach is significantly faster than the state-of-the-art (up to 84 times), allowing us to compute stable payments for 2000 agents in 50 minutes. We also develop a parallel version of our approach, which achieves a near-optimal speed-up in the number of processors used. Finally, our empirical analysis reveals new insights into the relationship between payments incurred by a user by virtue of its position in its social network and its role (rider or driver).","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123435342","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: 21
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