Proceedings of the 7th ACM conference on Recommender systems最新文献_第3页

Beyond friendship: the art, science and applications of recommending people to people in social networks 超越友谊:在社交网络中向他人推荐的艺术、科学和应用

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508064

L. Pizzato, Anmol Bhasin

{"title":"Beyond friendship: the art, science and applications of recommending people to people in social networks","authors":"L. Pizzato, Anmol Bhasin","doi":"10.1145/2507157.2508064","DOIUrl":"https://doi.org/10.1145/2507157.2508064","url":null,"abstract":"While Recommender Systems are powerful drivers of engagement and transactional utility in social networks, People recommenders are a fairly involved and diverse subdomain. Consider that movies are recommended to be watched, news is recommended to be read, people however, are recommended for a plethora of reasons -- such as recommendation of people to befriend, follow, partner, targets for an advertisement or service, recruiting, partnering romantically and to join thematic interest groups. This tutorial aims to first describe the problem domain, touch upon classical approaches like link analysis and collaborative filtering and then take a rapid deep dive into the unique aspects of this problem space like reciprocity, intent understanding of recommender and the recomendee, contextual people recommendations in communication flows and social referrals -- a paradigm for delivery of recommendations using the social graph. These aspects will be discussed in the context of published original work developed by the authors and their collaborators and in many cases deployed in massive-scale real world applications on professional networks such as LinkedIn.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122657958","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: 3

Selecting content-based features for collaborative filtering recommenders 为协同过滤推荐选择基于内容的特征

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507203

Royi Ronen, Noam Koenigstein, Elad Ziklik, Nir Nice

引用次数: 41

PEN RecSys: a personalized news recommender systems framework PEN RecSys:一个个性化的新闻推荐系统框架

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508218

Florent Garcin, B. Faltings

引用次数: 25

GAIN: web service for user tracking and preference learning - a smart TV use case GAIN:用于用户跟踪和偏好学习的web服务——一个智能电视用例

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508217

Jaroslav Kuchař, Tomáš Kliegr

引用次数: 8

Nonlinear latent factorization by embedding multiple user interests 嵌入多个用户兴趣的非线性潜在因子分解

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507209

J. Weston, Ron J. Weiss, H. Yee

引用次数: 72

Recommendation in heterogeneous information networks with implicit user feedback 隐含用户反馈的异构信息网络推荐

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507230

Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han

{"title":"Recommendation in heterogeneous information networks with implicit user feedback","authors":"Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han","doi":"10.1145/2507157.2507230","DOIUrl":"https://doi.org/10.1145/2507157.2507230","url":null,"abstract":"Recent studies suggest that by using additional user or item relationship information when building hybrid recommender systems, the recommendation quality can be largely improved. However, most such studies only consider a single type of relationship, e.g., social network. Notice that in many applications, the recommendation problem exists in an attribute-rich heterogeneous information network environment. In this paper, we study the entity recommendation problem in heterogeneous information networks. We propose to combine various relationship information from the network with user feedback to provide high quality recommendation results. The major challenge of building recommender systems in heterogeneous information networks is to systematically define features to represent the different types of relationships between entities, and learn the importance of each relationship type. In the proposed framework, we first use meta-path-based latent features to represent the connectivity between users and items along different paths in the related information network. We then define a recommendation model with such latent features and use Bayesian ranking optimization techniques to estimate the model. Empirical studies show that our approach outperforms several widely employed implicit feedback entity recommendation techniques.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133928274","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: 169

Personalized next-song recommendation in online karaokes 在线卡拉ok个性化推荐下一首歌曲

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507215

Xiang Wu, Qi Liu, Enhong Chen, Liang He, Jingsong Lv, Can Cao, Guoping Hu

引用次数: 66

Which app will you use next?: collaborative filtering with interactional context 接下来你会使用哪个应用程序?:具有交互上下文的协同过滤

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507186

Nagarajan Natarajan, D. Shin, I. Dhillon

{"title":"Which app will you use next?: collaborative filtering with interactional context","authors":"Nagarajan Natarajan, D. Shin, I. Dhillon","doi":"10.1145/2507157.2507186","DOIUrl":"https://doi.org/10.1145/2507157.2507186","url":null,"abstract":"The application a smart phone user will launch next intuitively depends on the sequence of apps used recently. More generally, when users interact with systems such as shopping websites or online radio, they click on items that are of interest in the current context. We call the sequence of clicks made in the current session interactional context. It is desirable for a recommender system to use the context set by the user to update recommendations. Most current context-aware recommender systems focus on a relatively less dynamic representational context defined by attributes such as season, location and tastes. In this paper, we study the problem of collaborative filtering with interactional context, where the goal is to make personalized and dynamic recommendations to a user engaged in a session. To this end, we propose the methodname algorithm that works in two stages. First, users are clustered by their transition behavior (one-step Markov transition probabilities between items), and cluster-level Markov models are computed. Then personalized PageRank is computed for a given user on the corresponding cluster Markov graph, with a personalization vector derived from the current context. We give an interpretation of the second stage of the algorithm as adding an appropriate context bias, in addition to click bias (or rating bias), to a classical neighborhood-based collaborative filtering model, where the neighborhood is determined from a Markov graph. Experimental results on two real-life datasets demonstrate the superior performance of our algorithm, where we achieve at least 20% (up to 37%) improvement over competitive methods in the recall level at top-20.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133016471","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: 96

Evaluating top-n recommendations "when the best are gone" “当最好的已经消失的时候”评估排名靠前的推荐

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507225

P. Cremonesi, F. Garzotto, Massimo Quadrana

引用次数: 7

Sage: recommender engine as a cloud service Sage:作为云服务的推荐引擎

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508221

Royi Ronen, Noam Koenigstein, Elad Ziklik, Mikael Sitruk, R. Yaari, Neta Haiby-Weiss

引用次数: 11