Proceedings of the 7th ACM conference on Recommender systems最新文献_第4页

Session details: Technical session: methods, algorithms, and theory I 技术部分:方法、算法和理论1

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249960

R. Burke

引用次数: 0

Location-aware music recommendation using auto-tagging and hybrid matching 使用自动标记和混合匹配的位置感知音乐推荐

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507180

Marius Kaminskas, F. Ricci, M. Schedl

引用次数: 90

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507181

M. S. Pera, Yiu-Kai Ng

{"title":"What to read next?: making personalized book recommendations for K-12 users","authors":"M. S. Pera, Yiu-Kai Ng","doi":"10.1145/2507157.2507181","DOIUrl":"https://doi.org/10.1145/2507157.2507181","url":null,"abstract":"Finding books that children/teenagers are interested in these days is a non-trivial task due to the diversity of topics covered in huge volumes of books with varied readability levels. Even though K-12 readers can turn to book recommenders to look for books, the recommended books may not satisfy their personal needs, since they could be beyond/below their readability levels or fail to match their topics of interest. To address these problems, we introduce BReK12, a book recommender that makes personalized suggestions tailored to each K-12 user U based on books available on a social book-marking site that (i) are similar in content to the ones that are known to be of interest to U, (ii) have been bookmarked by users with reading patterns similar to U's, and (iii) can be comprehended by U. BReK12 is an asset to its users, since it suggests books that are appealing to its users and at grade levels that they can cope with, which can increase their reading selection choices and motivate them to read. We have also developed ReLAT, the readability analysis tool employed by BReK12 to determine the grade level of books. ReLAT is novel, compared with existing readability formulas, since it can predict the grade level of a book even if an excerpt of the book is not available. We have conducted empirical studies which have verified the accuracy of ReLAT in predicting the grade level of a book and the effectiveness of BReK12 over existing baseline recommendation systems.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"41 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":"129801403","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: 31

Local context modeling with semantic pre-filtering 使用语义预过滤的局部上下文建模

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507218

Victor Codina, F. Ricci, Luigi Ceccaroni

引用次数: 18

Clustering-based factorized collaborative filtering 基于聚类的分解协同过滤

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507233

Nima Mirbakhsh, C. Ling

引用次数: 20

Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach 在准确性、相似性、多样性和长尾之间进行权衡:基于图的推荐方法

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507165

Lei Shi

{"title":"Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach","authors":"Lei Shi","doi":"10.1145/2507157.2507165","DOIUrl":"https://doi.org/10.1145/2507157.2507165","url":null,"abstract":"Improving recommendation accuracy is the mostly focused target of recommendation systems, while it has been increasingly recognized that accuracy is not enough as the only quality criterion. More concepts have been proposed recently to augment the evaluation dimensions, such as similarity, diversity, long-tail, etc. Simultaneously considering multiple criteria leads to a multi-task recommendation. In this paper, a graph-based recommendation approach is proposed to effectively and flexibly trade-off among them. Our approach is considered based a 1st order Markovian graph with transition probabilities between user-item pairs. A \"cost flow\" concept is proposed over the graph, so that items with lower costs are stronger recommended to a user. The cost flows are formulated in a recursive dynamic form, whose stability is proved to be guaranteed by appropriately lower-bounding the transition costs. Furthermore, a mixture of transition costs is designed by combining three ingredients related to long-tail, focusing degree and similarity. To evaluate the ingredients, we propose an orthogonal-sparse-orthogonal nonnegative matrix tri-factorization model and an efficient multiplicative algorithm. Empirical experiments on real-world data show promising results of our approach, which could be regarded as a general framework for other affects if transition costs are designed in various ways.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"26 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":"127660411","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: 58

Exploring temporal effects for location recommendation on location-based social networks 探索基于位置的社交网络上位置推荐的时间效应

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507182

Huiji Gao, Jiliang Tang, Xia Hu, Huan Liu

引用次数: 488

ReComment: towards critiquing-based recommendation with speech interaction ReComment:基于评论的语音交互推荐

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507161

Peter Grasch, A. Felfernig, Florian Reinfrank

引用次数: 41

Using geospatial metadata to boost collaborative filtering 利用地理空间元数据促进协同过滤

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507201

Alexander Ostrikov, L. Rokach, Bracha Shapira

引用次数: 4

You are what you consume: a bayesian method for personalized recommendations 你就是你所消费的:个性化推荐的贝叶斯方法

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507158

Konstantinos Babas, G. Chalkiadakis, E. Tripolitakis

引用次数: 10