Recommendation systems struggle to incorporate rich features, such as those derived from natural language and images. While humans can readily process this sort of information, they cannot not scale in the same way that statistical/ML models can. As a result, hybrid-algorithms that make recommendations based on the outputs of both computers and humans are becoming increasingly popular. This talk will explore novel methods for determining what features the human side of these systems should be processing. It will outline how experimental methods (borrowed from the behavioral sciences) can be used to this end, along with how the human recommendations may be improved as a result.
{"title":"Feature Selection For Human Recommenders","authors":"Katherine A. Livins","doi":"10.1145/2959100.2959123","DOIUrl":"https://doi.org/10.1145/2959100.2959123","url":null,"abstract":"Recommendation systems struggle to incorporate rich features, such as those derived from natural language and images. While humans can readily process this sort of information, they cannot not scale in the same way that statistical/ML models can. As a result, hybrid-algorithms that make recommendations based on the outputs of both computers and humans are becoming increasingly popular. This talk will explore novel methods for determining what features the human side of these systems should be processing. It will outline how experimental methods (borrowed from the behavioral sciences) can be used to this end, along with how the human recommendations may be improved as a result.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"67 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":"126614813","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}
Group recommender systems provide suggestions in contexts in which people operate in groups. The goal of this tutorial is to provide the RecSys audience with an overview on group recommendation. We will first formally introduce the problem of producing recommendations to groups, then present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges.
{"title":"Group Recommender Systems","authors":"Ludovico Boratto","doi":"10.1145/2959100.2959197","DOIUrl":"https://doi.org/10.1145/2959100.2959197","url":null,"abstract":"Group recommender systems provide suggestions in contexts in which people operate in groups. The goal of this tutorial is to provide the RecSys audience with an overview on group recommendation. We will first formally introduce the problem of producing recommendations to groups, then present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"2008 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":"125623416","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}
For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.
{"title":"Mendeley: Recommendations for Researchers","authors":"S. Vargas, Maya Hristakeva, Kris Jack","doi":"10.1145/2959100.2959116","DOIUrl":"https://doi.org/10.1145/2959100.2959116","url":null,"abstract":"For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's \"research operating system\". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.","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":"124943728","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}
Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization -- ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods.
{"title":"Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization","authors":"Jie Yang, Zhu Sun, A. Bozzon, Jie Zhang","doi":"10.1145/2959100.2959159","DOIUrl":"https://doi.org/10.1145/2959100.2959159","url":null,"abstract":"Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization -- ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"27 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":"124764919","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}
Query recommendation predominantly relies on search logs to use existing queries for recommendation, typically calculating query similarity metrics or transition probabilities from the log. While effective, such recommendations are limited to the queries, words, and phrases in the log. They hence do not recommend potentially useful, entirely novel queries. Recent query recommendation methods have proposed generating queries on a topical or thematic level, though current approaches are limited to generating single words. We propose a hybrid method for constructing multi-word queries in this generative sense. It uses Latent Dirichlet Allocation to generate a topic for exploration and skip-gram modeling to generate queries from the topic. According to additional evaluation metrics we present, our model improves diversity and has some room for improving relevance, yet offers an interesting avenue for query recommendation.
{"title":"Multi-Word Generative Query Recommendation Using Topic Modeling","authors":"M. Mitsui, C. Shah","doi":"10.1145/2959100.2959154","DOIUrl":"https://doi.org/10.1145/2959100.2959154","url":null,"abstract":"Query recommendation predominantly relies on search logs to use existing queries for recommendation, typically calculating query similarity metrics or transition probabilities from the log. While effective, such recommendations are limited to the queries, words, and phrases in the log. They hence do not recommend potentially useful, entirely novel queries. Recent query recommendation methods have proposed generating queries on a topical or thematic level, though current approaches are limited to generating single words. We propose a hybrid method for constructing multi-word queries in this generative sense. It uses Latent Dirichlet Allocation to generate a topic for exploration and skip-gram modeling to generate queries from the topic. According to additional evaluation metrics we present, our model improves diversity and has some room for improving relevance, yet offers an interesting avenue for query recommendation.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"69 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":"134155124","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}
This talk presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking to drive a quarter of the total engagement on Pinterest. Signals derived from user curation, the activity of users organizing content, are highly effective when used in conjunction with content based ranking. This will be an in-depth dive into the end-to-end system of Related Pins, a real-world implementation of an item-to-item hybrid recommendation system.
本次演讲介绍了Pinterest Related Pins，这是一个商品到商品的推荐系统，结合了协同过滤和基于内容的排名，推动了Pinterest总参与度的四分之一。当与基于内容的排名结合使用时，来自用户管理(用户组织内容的活动)的信号非常有效。这篇文章将深入介绍Related Pins的端到端系统，这是一个现实世界中商品对商品混合推荐系统的实现。
{"title":"Item-to-item Recommendations at Pinterest","authors":"Stephanie Rogers","doi":"10.1145/2959100.2959130","DOIUrl":"https://doi.org/10.1145/2959100.2959130","url":null,"abstract":"This talk presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking to drive a quarter of the total engagement on Pinterest. Signals derived from user curation, the activity of users organizing content, are highly effective when used in conjunction with content based ranking. This will be an in-depth dive into the end-to-end system of Related Pins, a real-world implementation of an item-to-item hybrid recommendation system.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"85 2 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":"126094362","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}
Jan Neumann, John Hannon, Claudio Riefolo, H. Sayyadi
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 3rd 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.
{"title":"3rd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2016)","authors":"Jan Neumann, John Hannon, Claudio Riefolo, H. Sayyadi","doi":"10.1145/2959100.2959198","DOIUrl":"https://doi.org/10.1145/2959100.2959198","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 3rd 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":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"28 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":"130851246","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}
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.
{"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}
Peter Brusilovsky, A. Felfernig, P. Lops, J. O'Donovan, G. Semeraro, N. Tintarev, M. Willemsen
As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems. Methodologies for evaluating these aspects are also within the scope of the workshop.
{"title":"RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems","authors":"Peter Brusilovsky, A. Felfernig, P. Lops, J. O'Donovan, G. Semeraro, N. Tintarev, M. Willemsen","doi":"10.1145/2959100.2959199","DOIUrl":"https://doi.org/10.1145/2959100.2959199","url":null,"abstract":"As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems. Methodologies for evaluating these aspects are also within the scope of the workshop.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"23 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":"123267135","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}
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
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.
{"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}
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