Proceedings of the 7th ACM conference on Recommender systems最新文献_第6页

Using maximum coverage to optimize recommendation systems in e-commerce 利用最大覆盖优化电子商务推荐系统

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507169

Mikael Hammar, Robin Karlsson, Bengt J. Nilsson

引用次数: 24

Session details: Technical session: beyond ratings 会议细节:技术会议:超越评级

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249964

M. Ester

引用次数: 0

Acquiring user profiles from implicit feedback in a conversational recommender system 从会话式推荐系统的隐式反馈中获取用户资料

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507217

Henry Blanco, F. Ricci

引用次数: 9

Personalised ranking with diversity 个性化排名与多样性

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507226

N. Hurley

引用次数: 42

Accuracy and robustness impacts of power user attacks on collaborative recommender systems 超级用户攻击对协同推荐系统的准确性和鲁棒性影响

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508070

C. E. Seminario

{"title":"Accuracy and robustness impacts of power user attacks on collaborative recommender systems","authors":"C. E. Seminario","doi":"10.1145/2507157.2508070","DOIUrl":"https://doi.org/10.1145/2507157.2508070","url":null,"abstract":"Power users, in a Collaborative Filtering (CF) Recommender System (RS) context, are those who can exert considerable influence over the recommendation outcomes presented to other users. RS operators encourage the existence of power user communities and leverage them to help fellow users make informed purchase decisions. Thus, RS research in this area has focused on power user selection and utilization to address challenges such as rating sparsity for new items or users. But, as ever, there remains the potential for corruption by power users who provide biased ratings. And because of the influence that power users wield, biased ratings they provide can have significant impacts on RS accuracy and robustness. In order to better understand this problem and develop solution strategies, our research is investigating the impact on RS predictions and top-N recommendation lists when power users provide biased ratings. The open areas of research we have explored are analyzing and evaluating power user selection techniques, statistically characterizing power users in order to create attack profiles, mounting power user attacks on new items, and using accuracy and robustness metrics to evaluate power user attacks. In the future, we plan to extend our initial research in power user selection, characterization, and evaluation, as well as generate attack profiles based on power user characteristics, mount power user attacks on user-based, item-based, and SVD-based CF systems, evaluate power user attacks, and generalize our work across different domains.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"15 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":"125164197","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: 16

Session details: Technical session: context-aware 会话细节:技术会话:上下文感知

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249959

S. Berkovsky

引用次数: 0

Recommending branded products from social media 从社交媒体上推荐品牌产品

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507170

Yongzheng Zhang, M. Pennacchiotti

引用次数: 31

Pairwise learning in recommendation: experiments with community recommendation on linkedin 推荐中的两两学习:linkedin上的社区推荐实验

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507175

Amit Sharma, B. Yan

{"title":"Pairwise learning in recommendation: experiments with community recommendation on linkedin","authors":"Amit Sharma, B. Yan","doi":"10.1145/2507157.2507175","DOIUrl":"https://doi.org/10.1145/2507157.2507175","url":null,"abstract":"Many online systems present a list of recommendations and infer user interests implicitly from clicks or other contextual actions. For modeling user feedback in such settings, a common approach is to consider items acted upon to be relevant to the user, and irrelevant otherwise. However, clicking some but not others conveys an implicit ordering of the presented items. Pairwise learning, which leverages such implicit ordering between a pair of items, has been successful in areas such as search ranking. In this work, we study whether pairwise learning can improve community recommendation. We first present two novel pairwise models adapted from logistic regression. Both offline and online experiments in a large real-world setting show that incorporating pairwise learning improves the recommendation performance. However, the improvement is only slight. We find that users' preferences regarding the kinds of communities they like can differ greatly, which adversely affect the effectiveness of features derived from pairwise comparisons. We therefore propose a probabilistic latent semantic indexing model for pairwise learning (Pairwise PLSI), which assumes a set of users' latent preferences between pairs of items. Our experiments show favorable results for the Pairwise PLSI model and point to the potential of using pairwise learning for community recommendation.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"49 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":"133073818","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: 25

Rating support interfaces to improve user experience and recommender accuracy 评级支持界面，以提高用户体验和推荐的准确性

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507188

Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, M. Willemsen, J. Riedl

引用次数: 42

xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance xCLiMF:优化具有多个相关级别的数据的预期倒数排名

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507227

Yue Shi, Alexandros Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic

引用次数: 58