Proceedings of the 9th ACM Conference on Recommender Systems最新文献_第4页

Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (#IntRS) 推荐系统界面和人工决策联合研讨会(#IntRS)

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798714

J. O'Donovan, N. Tintarev, A. Felfernig, Peter Brusilovsky, G. Semeraro, P. Lops

引用次数: 10

OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap OpenStreetMap空间实体类别自动推荐的OSMRec工具

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796555

Nikos Karagiannakis, G. Giannopoulos, Dimitrios Skoutas, Spiros Athanasiou

引用次数: 16

Risk-Hedged Venture Capital Investment Recommendation 风险对冲风险投资建议

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800181

Xiaoxue Zhao, Weinan Zhang, Jun Wang

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引用次数: 13

Exploring Statistical Language Models for Recommender Systems 探索推荐系统的统计语言模型

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796547

Daniel Valcarce

引用次数: 15

"I like to explore sometimes": Adapting to Dynamic User Novelty Preferences “我有时喜欢探索”:适应动态用户新奇偏好

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800172

Komal Kapoor, Vikas Kumar, L. Terveen, J. Konstan, P. Schrater

引用次数: 100

Overview of ACM RecSys CrowdRec 2015 Workshop: Crowdsourcing and Human Computation for Recommender Systems ACM RecSys CrowdRec 2015研讨会综述:推荐系统的众包和人工计算

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2798719

M. Larson, D. Tikk, R. Turrin

引用次数: 1

Applying Differential Privacy to Matrix Factorization 微分隐私在矩阵分解中的应用

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800173

Arnaud Berlioz, Arik Friedman, M. Kâafar, R. Boreli, S. Berkovsky

引用次数: 118

It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering 两个人跳探戈:跨领域协同过滤的领域对探索

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800188

Shaghayegh Sherry Sahebi, Peter Brusilovsky

{"title":"It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering","authors":"Shaghayegh Sherry Sahebi, Peter Brusilovsky","doi":"10.1145/2792838.2800188","DOIUrl":"https://doi.org/10.1145/2792838.2800188","url":null,"abstract":"As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"2 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":"127822741","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: 34

A Personalised Reader for Crowd Curated Content 人群策划内容的个性化读者

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2796552

G. Kazai, Daoud Clarke, Iskander Yusof, M. Venanzi

引用次数: 4

Top-N Recommendation for Shared Accounts 共享帐户Top-N推荐

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800170

Koen Verstrepen, Bart Goethals

引用次数: 32