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

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Towards Automatic Meal Plan Recommendations for Balanced Nutrition 迈向均衡营养的自动膳食计划建议
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799665
David Elsweiler, Morgan Harvey
{"title":"Towards Automatic Meal Plan Recommendations for Balanced Nutrition","authors":"David Elsweiler, Morgan Harvey","doi":"10.1145/2792838.2799665","DOIUrl":"https://doi.org/10.1145/2792838.2799665","url":null,"abstract":"Food recommenders have been touted as a useful tool to help people achieve a healthy diet. Here we incorporate nutrition into the recommender problem by examining the feasibility of algorithmically creating daily meal plans for a sample of user profiles (n=100), combined with a diverse set of food preference data (n=64) collected in a natural setting. Our analyses demonstrate it is possible to recommend plans for a large percentage of users which meet the guidelines set out by international health agencies","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"22 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":"129104781","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
LocalRec'15: Workshop on Location-Aware Recommendations LocalRec'15:位置感知建议研讨会
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798720
Panagiotis Bouros, N. Lathia, M. Renz, F. Ricci, Dimitris Sacharidis
{"title":"LocalRec'15: Workshop on Location-Aware Recommendations","authors":"Panagiotis Bouros, N. Lathia, M. Renz, F. Ricci, Dimitris Sacharidis","doi":"10.1145/2792838.2798720","DOIUrl":"https://doi.org/10.1145/2792838.2798720","url":null,"abstract":"The amount of available geo-referenced data has seen a dramatic explosion over the past few years. Human activities now generate digital traces that are annotated with location data, enabling the collection of rich information about people's interests and habits. This torrent of geo-referenced data provides a tremendous potential to augment recommender systems. The LocalRec'15 workshop brings together scholars from location-based services and recommender systems, and seeks to set out new trends and research directions.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"13 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121008508","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: 4
Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging 顺境逆境:社会标签协同过滤的近因效应研究
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799682
Santiago Larrain, C. Trattner, Denis Parra, Eduardo Graells-Garrido, K. Nørvåg
{"title":"Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging","authors":"Santiago Larrain, C. Trattner, Denis Parra, Eduardo Graells-Garrido, K. Nørvåg","doi":"10.1145/2792838.2799682","DOIUrl":"https://doi.org/10.1145/2792838.2799682","url":null,"abstract":"In this paper, we present work-in-progress of a recently started project that aims at studying the effect of time in recommender systems in the context of social tagging. Despite the existence of previous work in this area, no research has yet made an extensive evaluation and comparison of time-aware recommendation methods. With this motivation, this paper presents results of a study where we focused on understanding (i) \"when\" to use the temporal information into traditional collaborative filtering (CF) algorithms, and (ii) \"how\" to weight the similarity between users and items by exploring the effect of different time-decay functions. As the results of our extensive evaluation conducted over five social tagging systems (Delicious, BibSonomy, CiteULike, MovieLens, and Last.fm) suggest, the step (when) in which time is incorporated in the CF algorithm has substantial effect on accuracy, and the type of time-decay function (how) plays a role on accuracy and coverage mostly under pre-filtering on user-based CF, while item-based shows stronger stability over the experimental conditions.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"6 Pt A 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":"116775789","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
Kibitz: End-to-End Recommendation System Builder Kibitz:端到端推荐系统构建器
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796557
Quanquan C. Liu, David R Karger
{"title":"Kibitz: End-to-End Recommendation System Builder","authors":"Quanquan C. Liu, David R Karger","doi":"10.1145/2792838.2796557","DOIUrl":"https://doi.org/10.1145/2792838.2796557","url":null,"abstract":"Kibitz (kibitz.csail.mit.edu) is a web application and recommendation system framework that helps inexperienced and novice programmers to build recommenders without the need to program the back end for the system. The author uploads a table of items, and Kibitz produces a collaborative-filtering recommender for the uploaded items. The recommender can be hosted by Kibitz or downloaded and customized as a set of static pages hosted on the author's personal web domain. Developers who want to avoid the hassle of writing their own recommender back end may choose to link their websites to our service through our easy to use API. A demo of our system can be found at kibitz.csail.mit.edu/video_demo/.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"71 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":"128122901","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
Overlapping Community Regularization for Rating Prediction in Social Recommender Systems 基于重叠社区正则化的社会推荐系统评级预测
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800171
Hui Li, Dingming Wu, Wenbin Tang, N. Mamoulis
{"title":"Overlapping Community Regularization for Rating Prediction in Social Recommender Systems","authors":"Hui Li, Dingming Wu, Wenbin Tang, N. Mamoulis","doi":"10.1145/2792838.2800171","DOIUrl":"https://doi.org/10.1145/2792838.2800171","url":null,"abstract":"Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user's rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recent approaches use data from social networks to improve accuracy. However, most of the social-network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this paper, we propose two alternative models that incorporate the overlapping community regularization into the matrix factorization framework. Our empirical study on four real datasets shows that our approaches outperform the state-of-the-art algorithms in both traditional and social-network based recommender systems regarding both cold-start users and normal users.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"67 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":"115664708","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: 77
Putting Users in Control of their Recommendations 让用户控制他们的推荐
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800179
F. M. Harper, F. Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, L. Terveen
{"title":"Putting Users in Control of their Recommendations","authors":"F. M. Harper, F. Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, L. Terveen","doi":"10.1145/2792838.2800179","DOIUrl":"https://doi.org/10.1145/2792838.2800179","url":null,"abstract":"The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like \"show more popular items\". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"12 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":"114358836","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: 83
Listener-Inspired Automated Music Playlist Generation 听众启发的自动音乐播放列表生成
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796548
Andreu Vall
{"title":"Listener-Inspired Automated Music Playlist Generation","authors":"Andreu Vall","doi":"10.1145/2792838.2796548","DOIUrl":"https://doi.org/10.1145/2792838.2796548","url":null,"abstract":"The objective of this PhD research is to deepen the understanding of how people listen to music and construct playlists. We believe that further insights into such mechanisms can lead to enhanced music recommendations. We research on the exploitation of user-generated data in the context of on-line music services, since it constitutes a rich and increasing source of information of user behavior. The research carried out so far has centered on the scenario of producing a single artist recommendation. Concretely, in this paper we show how to mitigate the cold-start problem for new artists, elaborating on our findings on the combined effect of users' listening histories and users' tagging activity. As future research, we will investigate how improved techniques to exploit user-generated data can also be applied to the task of producing sequential recommendations, like playlists. We are particulary interested in creating music playlists similarly as users would do, and in finding mechanisms to make such music streams adapt to users' feedback on-line.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"38 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":"115770349","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: 23
Selection and Ordering of Linear Online Video Ads 线性网络视频广告的选择与排序
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800194
Wreetabrata Kar, Viswanathan Swaminathan, Paulo Albuquerque
{"title":"Selection and Ordering of Linear Online Video Ads","authors":"Wreetabrata Kar, Viswanathan Swaminathan, Paulo Albuquerque","doi":"10.1145/2792838.2800194","DOIUrl":"https://doi.org/10.1145/2792838.2800194","url":null,"abstract":"This paper studies the selection and ordering of in-stream ads in videos shown in online content publishers. We propose an allocation algorithm that uses a collective measure of price and quality for each ad and factors in slot-specific continuation probabilities to maximize publisher revenue. The algorithm is based on cascade models and uses a dynamic programming method to assign linear (video) ads to slots in an online video. The approach accounts for the negative externality created by lower quality ads placed in a video, leading to viewer exit and thereby preventing the publisher from showing the subsequent ads scheduled in that session. Our algorithm is scalable and suited for real-time applications. A large log of viewer activity from a video ad platform is used to empirically test the algorithm. A series of simulations show that our algorithm, when compared to other algorithms currently practiced in industry, generates more revenue for the publisher and increases viewer retention.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"202 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":"123038411","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
Assessing Expertise in the Enterprise: The Recommender Point of View 评估企业的专业知识:推荐人的观点
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799497
A. Mojsilovic, Kush R. Varshney
{"title":"Assessing Expertise in the Enterprise: The Recommender Point of View","authors":"A. Mojsilovic, Kush R. Varshney","doi":"10.1145/2792838.2799497","DOIUrl":"https://doi.org/10.1145/2792838.2799497","url":null,"abstract":"Some of the largest worldwide employers today are knowledge-based enterprises whose most important asset is human capital. Knowledge workers are unique, each having individualized skills, competencies and expertise, which constantly evolve and expand. Managing and planning for such a workforce critically depends on the ability to construct complete, accurate, and real-time representation and inventory of the expertise of employees in a form that integrates with business processes. In this session Saška will describe how enterprise expertise assessment process can be posed as predictive modeling and recommendation problem, and will present results and lessons learned from an actual deployment of IBM Expertise, a corporate-wide expertise recommendation and management system.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"4 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":"125544313","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
Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015) 第二届基于内容的推荐系统新趋势研讨会(CBRecSys 2015)
Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798718
Toine Bogers, M. Koolen
{"title":"Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)","authors":"Toine Bogers, M. Koolen","doi":"10.1145/2792838.2798718","DOIUrl":"https://doi.org/10.1145/2792838.2798718","url":null,"abstract":"While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2015 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"1 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":"130394082","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: 4
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