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}
Google Play is a seamless approach to digital entertainment on all of your devices. It gives you one place to find, enjoy and share your favorite entertainment, from apps to movies, music, books and more, on the web or any device. With more than 1 billion active users in 190+ countries around the world, Play is an important distribution platform for developers to build a global audience. More than 50 billion apps-have been downloaded from Google Play. However, generating personalized recommendations for different kind of content is a complex technical and product problem. Each of Play verticals (apps, games, books, movies, music) has different business goals, metrics to optimize, and user behavior. In this talk, we'll present an overview of how Play recommendations work across these verticals, how we evaluate our results, and the impact of deep neural networks in improving recommendations.
Google Play是你在所有设备上进行数字娱乐的无缝途径。它给你一个地方找到，享受和分享你最喜欢的娱乐，从应用程序到电影，音乐，书籍和更多，在网络或任何设备上。Play在全球190多个国家拥有超过10亿活跃用户，是开发者打造全球用户的重要分销平台。Google Play的应用程序下载量已经超过500亿次。然而，为不同类型的内容生成个性化推荐是一个复杂的技术和产品问题。每个Play垂直领域(游戏邦注:包括应用、游戏、书籍、电影和音乐)都有不同的商业目标、需要优化的指标和用户行为。在本次演讲中，我们将概述Play推荐如何在这些垂直领域中发挥作用，我们如何评估我们的结果，以及深度神经网络在改进推荐方面的影响。
{"title":"Multi-corpus Personalized Recommendations on Google Play","authors":"L. Koc, C. Master","doi":"10.1145/2959100.2959129","DOIUrl":"https://doi.org/10.1145/2959100.2959129","url":null,"abstract":"Google Play is a seamless approach to digital entertainment on all of your devices. It gives you one place to find, enjoy and share your favorite entertainment, from apps to movies, music, books and more, on the web or any device. With more than 1 billion active users in 190+ countries around the world, Play is an important distribution platform for developers to build a global audience. More than 50 billion apps-have been downloaded from Google Play. However, generating personalized recommendations for different kind of content is a complex technical and product problem. Each of Play verticals (apps, games, books, movies, music) has different business goals, metrics to optimize, and user behavior. In this talk, we'll present an overview of how Play recommendations work across these verticals, how we evaluate our results, and the impact of deep neural networks in improving recommendations.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"54 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":"125260415","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}
F. Abel, A. Benczúr, Daniel Kohlsdorf, M. Larson, Róbert Pálovics
The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge.
{"title":"RecSys Challenge 2016: Job Recommendations","authors":"F. Abel, A. Benczúr, Daniel Kohlsdorf, M. Larson, Róbert Pálovics","doi":"10.1145/2959100.2959207","DOIUrl":"https://doi.org/10.1145/2959100.2959207","url":null,"abstract":"The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"84 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":"121235556","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}
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}
Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an offline evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not appropriate to select the best algorithm for field deployment. The goal of this work is therefore to compare the offline and the online evaluation methodology with the same study participants, i.e. a within users experimental design. This paper presents empirical evidence that the ranking of algorithms based on offline accuracy measurements clearly contradicts the results from the online study with the same set of users. Thus the external validity of the most commonly applied evaluation methodology is not guaranteed.
{"title":"Contrasting Offline and Online Results when Evaluating Recommendation Algorithms","authors":"Marco Rossetti, Fabio Stella, M. Zanker","doi":"10.1145/2959100.2959176","DOIUrl":"https://doi.org/10.1145/2959100.2959176","url":null,"abstract":"Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an offline evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not appropriate to select the best algorithm for field deployment. The goal of this work is therefore to compare the offline and the online evaluation methodology with the same study participants, i.e. a within users experimental design. This paper presents empirical evidence that the ranking of algorithms based on offline accuracy measurements clearly contradicts the results from the online study with the same set of users. Thus the external validity of the most commonly applied evaluation methodology is not guaranteed.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"146 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":"114642369","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}
The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of recommender systems in the work of Vargas et al. The framework considers a set of aspects associated with items to be recommended. For instance, aspects may correspond to genres in movie recommendations. The framework depends on input aspect model consisting of item selection or relevance probabilities, given an aspect, and user intents, in the form of probabilities that the user is interested in each aspect. In this paper, we examine a number of input aspect models and evaluate the impact that different models have on the framework. In particular, we propose a constrained PLSA model that allows for interpretable output, in terms of known aspects, while achieving greater performance that the explicit co-occurrence counting method used in previous work. We evaluate the proposed models using a well-known MovieLens dataset for which item genres are available.
{"title":"Intent-Aware Diversification Using a Constrained PLSA","authors":"Jacek Wasilewski, N. Hurley","doi":"10.1145/2959100.2959177","DOIUrl":"https://doi.org/10.1145/2959100.2959177","url":null,"abstract":"The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of recommender systems in the work of Vargas et al. The framework considers a set of aspects associated with items to be recommended. For instance, aspects may correspond to genres in movie recommendations. The framework depends on input aspect model consisting of item selection or relevance probabilities, given an aspect, and user intents, in the form of probabilities that the user is interested in each aspect. In this paper, we examine a number of input aspect models and evaluate the impact that different models have on the framework. In particular, we propose a constrained PLSA model that allows for interpretable output, in terms of known aspects, while achieving greater performance that the explicit co-occurrence counting method used in previous work. We evaluate the proposed models using a well-known MovieLens dataset for which item genres are available.","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":"114771622","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}
Pairwise learning-to-rank algorithms have been shown to allow recommender systems to leverage unary user feedback. We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. The feedback types are drawn from different "channels", in which users interact with items (e.g., clicks, likes, listens, follows, and purchases). We build on the insight that different kinds of feedback, e.g., a click versus a like, reflect different levels of commitment or preference. Our approach differs from previous work in that it exploits multiple sources of feedback simultaneously during the training process. The novelty of MF-BPR is an extended sampling method that equates feedback sources with "levels" that reflect the expected contribution of the signal. We demonstrate the effectiveness of our approach with a series of experiments carried out on three datasets containing multiple types of feedback. Our experimental results demonstrate that with a right sampling method, MF-BPR outperforms BPR in terms of accuracy. We find that the advantage of MF-BPR lies in its ability to leverage level information when sampling negative items.
{"title":"Bayesian Personalized Ranking with Multi-Channel User Feedback","authors":"B. Loni, Roberto Pagano, M. Larson, A. Hanjalic","doi":"10.1145/2959100.2959163","DOIUrl":"https://doi.org/10.1145/2959100.2959163","url":null,"abstract":"Pairwise learning-to-rank algorithms have been shown to allow recommender systems to leverage unary user feedback. We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. The feedback types are drawn from different \"channels\", in which users interact with items (e.g., clicks, likes, listens, follows, and purchases). We build on the insight that different kinds of feedback, e.g., a click versus a like, reflect different levels of commitment or preference. Our approach differs from previous work in that it exploits multiple sources of feedback simultaneously during the training process. The novelty of MF-BPR is an extended sampling method that equates feedback sources with \"levels\" that reflect the expected contribution of the signal. We demonstrate the effectiveness of our approach with a series of experiments carried out on three datasets containing multiple types of feedback. Our experimental results demonstrate that with a right sampling method, MF-BPR outperforms BPR in terms of accuracy. We find that the advantage of MF-BPR lies in its ability to leverage level information when sampling negative items.","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":"128966792","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}
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}
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