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Proceedings of the 7th ACM conference on Recommender systems最新文献

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Recommending patents based on latent topics 基于潜在主题推荐专利
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507232
Ralf Krestel, Padhraic Smyth
{"title":"Recommending patents based on latent topics","authors":"Ralf Krestel, Padhraic Smyth","doi":"10.1145/2507157.2507232","DOIUrl":"https://doi.org/10.1145/2507157.2507232","url":null,"abstract":"The availability of large volumes of granted patents and applications, all publicly available on the Web, enables the use of sophisticated text mining and information retrieval methods to facilitate access and analysis of patents. In this paper we investigate techniques to automatically recommend patents given a query patent. This task is critical for a variety of patent-related analysis problems such as finding relevant citations, research of relevant prior art, and infringement analysis. We investigate the use of latent Dirichlet allocation and Dirichlet multinomial regression to represent patent documents and to compute similarity scores. We compare our methods with state-of-the-art document representations and retrieval techniques and demonstrate the effectiveness of our approach on a collection of US patent publications.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"3 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":"115680487","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: 19
Catch-up TV recommendations: show old favourites and find new ones 追赶电视推荐:展示旧的最爱，寻找新的
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507204
Mengxi Xu, S. Berkovsky, S. Ardon, Sipat Triukose, A. Mahanti, I. Koprinska
{"title":"Catch-up TV recommendations: show old favourites and find new ones","authors":"Mengxi Xu, S. Berkovsky, S. Ardon, Sipat Triukose, A. Mahanti, I. Koprinska","doi":"10.1145/2507157.2507204","DOIUrl":"https://doi.org/10.1145/2507157.2507204","url":null,"abstract":"Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"241 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":"115590247","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: 27
The curated web: a recommendation challenge 精心策划的网络:一个推荐挑战
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507216
Z. Saaya, Rachael Rafter, M. Schaal, Barry Smyth
{"title":"The curated web: a recommendation challenge","authors":"Z. Saaya, Rachael Rafter, M. Schaal, Barry Smyth","doi":"10.1145/2507157.2507216","DOIUrl":"https://doi.org/10.1145/2507157.2507216","url":null,"abstract":"In this paper we consider the application of content-based recommendation techniques to web curation services which allow users to curate and share topical collections of content (e.g. images, news, web pages etc.). Curation services like Pinterest are now a mainstay of the modern web and present a range of interesting recommendation challenges. In this paper we consider the task of recommending collections to users and evaluate a range of different content-based techniques across a variety of content signals. We present the results of a large-scale evaluation using data from the Scoop.it web page curation service","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"28 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":"125158324","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: 8
Retargeted matrix factorization for collaborative filtering 协同过滤的重目标矩阵分解
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507185
Oluwasanmi Koyejo, Sreangsu Acharyya, Joydeep Ghosh
{"title":"Retargeted matrix factorization for collaborative filtering","authors":"Oluwasanmi Koyejo, Sreangsu Acharyya, Joydeep Ghosh","doi":"10.1145/2507157.2507185","DOIUrl":"https://doi.org/10.1145/2507157.2507185","url":null,"abstract":"This paper introduces retargeted matrix factorization (R-MF); a novel approach for learning the user-wise ranking of items in the context of collaborative filtering. R-MF learns to rank by \"retargeting\" the item ratings of each user, searching for a monotonic transformation of the ratings that results in a better fit while preserving the ranked order of each user's ratings. The retargeting is combined with an underlying matrix factorization regression model that couples the user-wise rankings to exploit shared low dimensional structure. We show that R-MF recovers a unique solution under mild conditions, and propose a simple and efficient optimization scheme that alternates between retargeting the ratings subject to ordering constraints, and matrix factorization regression. The retargeting step is independent for each user, and is trivially parallelized. The ranking performance of retargeted matrix factorization is evaluated on benchmark movie recommendation datasets and results in superior ranking performance compared to collaborative filtering algorithms specifically designed to optimize ranking metrics.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"33 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":"116661899","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: 22
Prior ratings: a new information source for recommender systems in e-commerce 先验评级:电子商务推荐系统的新信息源
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507200
G. Guo, Jie Zhang, Daniel Thalmann, N. Yorke-Smith
{"title":"Prior ratings: a new information source for recommender systems in e-commerce","authors":"G. Guo, Jie Zhang, Daniel Thalmann, N. Yorke-Smith","doi":"10.1145/2507157.2507200","DOIUrl":"https://doi.org/10.1145/2507157.2507200","url":null,"abstract":"Lack of motivation to provide ratings and eligibility to rate generally only after purchase restrain the effectiveness of recommender systems and contribute to the well-known data sparsity and cold start problems. This paper proposes a new information source for recommender systems, called prior ratings. Prior ratings are based on users' experiences of virtual products in a mediated environment, and they can be submitted prior to purchase. A conceptual model of prior ratings is proposed, integrating the environmental factor presence whose effects on product evaluation have not been studied previously. A user study conducted in website and virtual store modalities demonstrates the validity of the conceptual model, in that users are more willing and confident to provide prior ratings in virtual environments.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114119846","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: 5
The fifth ACM RecSys workshop on recommender systems and the social web 第五届ACM RecSys推荐系统和社交网络研讨会
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508001
B. Mobasher, D. Jannach, Werner Geyer, J. Freyne, A. Hotho, S. Anand, Ido Guy
{"title":"The fifth ACM RecSys workshop on recommender systems and the social web","authors":"B. Mobasher, D. Jannach, Werner Geyer, J. Freyne, A. Hotho, S. Anand, Ido Guy","doi":"10.1145/2507157.2508001","DOIUrl":"https://doi.org/10.1145/2507157.2508001","url":null,"abstract":"","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"25 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":"114302683","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: 0
Probabilistic collaborative filtering with negative cross entropy 负交叉熵的概率协同滤波
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507191
Alejandro Bellogín, Javier Parapar, P. Castells
{"title":"Probabilistic collaborative filtering with negative cross entropy","authors":"Alejandro Bellogín, Javier Parapar, P. Castells","doi":"10.1145/2507157.2507191","DOIUrl":"https://doi.org/10.1145/2507157.2507191","url":null,"abstract":"Relevance-Based Language Models are an effective IR approach which explicitly introduces the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models have shown to achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. In this paper we propose a novel adaptation of this language modeling approach to rating-based Collaborative Filtering. In a memory-based approach, we apply the model to the formation of user neighbourhoods, and the generation of recommendations based on such neighbourhoods. We report experimental results where our method outperforms other standard memory-based algorithms in terms of ranking precision.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"31 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":"124950671","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: 6
Session details: Technical session: media recommendation 技术环节:媒体推荐
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249962
B. Mobasher
{"title":"Session details: Technical session: media recommendation","authors":"B. Mobasher","doi":"10.1145/3249962","DOIUrl":"https://doi.org/10.1145/3249962","url":null,"abstract":"","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"66 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":"125100634","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: 0
Efficient top-n recommendation for very large scale binary rated datasets 有效的top-n推荐非常大规模的二进制评级数据集
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507189
F. Aiolli
{"title":"Efficient top-n recommendation for very large scale binary rated datasets","authors":"F. Aiolli","doi":"10.1145/2507157.2507189","DOIUrl":"https://doi.org/10.1145/2507157.2507189","url":null,"abstract":"We present a simple and scalable algorithm for top-N recommendation able to deal with very large datasets and (binary rated) implicit feedback. We focus on memory-based collaborative filtering algorithms similar to the well known neighboor based technique for explicit feedback. The major difference, that makes the algorithm particularly scalable, is that it uses positive feedback only and no explicit computation of the complete (user-by-user or item-by-item) similarity matrix needs to be performed. The study of the proposed algorithm has been conducted on data from the Million Songs Dataset (MSD) challenge whose task was to suggest a set of songs (out of more than 380k available songs) to more than 100k users given half of the user listening history and complete listening history of other 1 million people. In particular, we investigate on the entire recommendation pipeline, starting from the definition of suitable similarity and scoring functions and suggestions on how to aggregate multiple ranking strategies to define the overall recommendation. The technique we are proposing extends and improves the one that already won the MSD challenge last year.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"47 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":"124892264","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: 111
Session details: Technical session: scalability 会话详细信息:技术会话:可扩展性
Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249966
P. Cremonesi
{"title":"Session details: Technical session: scalability","authors":"P. Cremonesi","doi":"10.1145/3249966","DOIUrl":"https://doi.org/10.1145/3249966","url":null,"abstract":"","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"27 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":"133689883","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: 0
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