Proceedings of the 7th ACM conference on Recommender systems最新文献_第2页

Spatial topic modeling in online social media for location recommendation 面向网络社交媒体位置推荐的空间主题建模

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507174

Bo Hu, M. Ester

引用次数: 194

Topic diversity in tag recommendation 标签推荐中的主题多样性

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507184

F. Belém, Rodrygo L. T. Santos, J. Almeida, Marcos André Gonçalves

{"title":"Topic diversity in tag recommendation","authors":"F. Belém, Rodrygo L. T. Santos, J. Almeida, Marcos André Gonçalves","doi":"10.1145/2507157.2507184","DOIUrl":"https://doi.org/10.1145/2507157.2507184","url":null,"abstract":"Tag recommendation approaches have historically focused on maximizing the relevance of the recommended tags for a given object, such as a movie or a song. Nevertheless, different users may be interested in the same object for different reasons---for instance, the Star Wars movies may appeal to both adventure as well as to fantasy movie fans. In this situation, a sensible strategy is to provide a user with diverse recommendations of how to tag the object. In this paper, we address the problem of recommending relevant and diverse tags as a ranking problem. In particular, we propose a novel tag recommendation approach that explicitly takes into account the possible topics (e.g., categories) underlying an object in order to promote tags with high coverage and low redundancy with respect to these topics. We thoroughly evaluate our proposed approach using data collected from two popular Web 2.0 applications, namely, LastFM and MovieLens. Our experimental results attest the effectiveness of our approach at promoting more relevant and diverse tags in contrast to state-of-the-art relevance-based methods as well as a recently proposed method that takes both relevance and diversity into account.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"294 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":"132135618","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

Agent-based computational investing recommender system 基于agent的计算投资推荐系统

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508072

M. Taghavi, Kaveh Bakhtiyari, E. Scavino

引用次数: 27

Learning to rank recommendations with the k-order statistic loss 学习用k阶统计量损失对推荐进行排序

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507210

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

{"title":"Learning to rank recommendations with the k-order statistic loss","authors":"J. Weston, H. Yee, Ron J. Weiss","doi":"10.1145/2507157.2507210","DOIUrl":"https://doi.org/10.1145/2507157.2507210","url":null,"abstract":"Making recommendations by learning to rank is becoming an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked list. In this work we present a family of loss functions, the k-order statistic loss, that includes these previous approaches as special cases, and also derives new ones that we show to be useful. In particular, we present (i) a new variant that more accurately optimizes precision at k, and (ii) a novel procedure of optimizing the mean maximum rank, which we hypothesize is useful to more accurately cover all of the user's tastes. The general approach works by sampling N positive items, ordering them by the score assigned by the model, and then weighting the example as a function of this ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations, where we obtain improvements for computable metrics, and in the YouTube case, increased user click through and watch duration when deployed live on www.youtube.com.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"237 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":"124605983","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: 65

Session details: Technical session: user experience 会话详情:技术会话:用户体验

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249963

D. Jannach

引用次数: 0

Workshop on reproducibility and replication in recommender systems evaluation: RepSys 推荐系统评价中的再现性和复制讲习班:RepSys

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508006

Alejandro Bellogín, P. Castells, A. Said, D. Tikk

引用次数: 8

Context-aware review helpfulness rating prediction 上下文感知审查，有用性评级预测

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507183

Jiliang Tang, Huiji Gao, Xia Hu, Huan Liu

{"title":"Context-aware review helpfulness rating prediction","authors":"Jiliang Tang, Huiji Gao, Xia Hu, Huan Liu","doi":"10.1145/2507157.2507183","DOIUrl":"https://doi.org/10.1145/2507157.2507183","url":null,"abstract":"Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"1 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":"129271599","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: 81

Understanding and improving relational matrix factorization in recommender systems 理解和改进推荐系统中的关系矩阵分解

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507178

Li Pu, B. Faltings

引用次数: 17

First workshop on large-scale recommender systems: research and best practice(LSRS 2013) 第一次关于大规模推荐系统的研讨会:研究和最佳实践(LSRS 2013)

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508007

Tao Ye, Danny Bickson, Quan Yuan

引用次数: 2

Hidden factors and hidden topics: understanding rating dimensions with review text 隐藏的因素和隐藏的主题:用评论文本理解评级维度

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507163

Julian McAuley, J. Leskovec

{"title":"Hidden factors and hidden topics: understanding rating dimensions with review text","authors":"Julian McAuley, J. Leskovec","doi":"10.1145/2507157.2507163","DOIUrl":"https://doi.org/10.1145/2507157.2507163","url":null,"abstract":"In order to recommend products to users we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to identify that the book is about wizards, as well as the user's level of interest in wizardry. User feedback is required to discover these latent product and user dimensions. Such feedback often comes in the form of a numeric rating accompanied by review text. However, traditional methods often discard review text, which makes user and product latent dimensions difficult to interpret, since they ignore the very text that justifies a user's rating. In this paper, we aim to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics (such as those learned by topic models like LDA). Our approach has several advantages. Firstly, we obtain highly interpretable textual labels for latent rating dimensions, which helps us to `justify' ratings with text. Secondly, our approach more accurately predicts product ratings by harnessing the information present in review text; this is especially true for new products and users, who may have too few ratings to model their latent factors, yet may still provide substantial information from the text of even a single review. Thirdly, our discovered topics can be used to facilitate other tasks such as automated genre discovery, and to identify useful and representative reviews.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"34 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":"122631844","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: 1644