Papers by Xavier Amatriain
International Joint Conference on Artificial Intelligence, 2009
Collaborative filtering (CF) algorithms, which generate recommendations for web users by predicti... more Collaborative filtering (CF) algorithms, which generate recommendations for web users by predicting user-item ratings, are often evaluated according to their predictions; in this context the problem of generating recommendations can be formulated as one of fitting a community of users to the best set of predictors. However, the data used to perform CF is sparse, and accuracy is limited by both the quantity and quality of information available. Mining the web has the potential to address these issues: the quality and quantity of ratings can be incremented by collecting external sources of rating information. In this work we introduce a method to perform CF with external data sources; furthermore, we show that a community of users can be partitioned according to what external source acts as a better predictor of each user's preferences. In particular, we find that a single kNN predictor can achieve remarkably high prediction accuracy if the data sources are selected optimally: designing a recommender system can thus be approached with the focus on data quality rather than algorithmic method.
Collaborative ltering (CF) algorithms, which gen- erate recommendations for web users by predict-... more Collaborative ltering (CF) algorithms, which gen- erate recommendations for web users by predict- ing user-item ratings, are often evaluated according to their predictions; in this context the problem of generating recommendations can be formulated as one of tting a community of users to the best set of predictors. However, the data used to perform CF is sparse, and accuracy is
Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10, 2010
Collaborative Filtering (CF) algorithms, used to build webbased recommender systems, are often ev... more Collaborative Filtering (CF) algorithms, used to build webbased recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the system's top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.
2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2010
Expert Collaborative Filtering is an approach to recommender systems in which recommendations for... more Expert Collaborative Filtering is an approach to recommender systems in which recommendations for users are derived from ratings coming from domain experts rather than peers. In this paper we present an implementation of this approach in the music domain. We show the applicability of the model in this setting, and show how it addresses many of the shortcomings in traditional Collaborative Filtering such as possible privacy concerns. We also describe a number of technologies and an architectural solution based on REST and the use of Linked Data that can be used to implement a completely distributed and privacy-preserving recommender system.
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
ACM SIGKDD Explorations Newsletter, 2013
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14, 2014
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09, 2009
Nearest-neighbor collaborative filtering provides a successful means of generating recommendation... more Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement conference - IMC '08, 2008
For half a century, television has been a dominant and pervasive mass media, driving many technol... more For half a century, television has been a dominant and pervasive mass media, driving many technological advances. Despite its widespread usage and importance to emerging applications, the ingrained TV viewing habits are not completely understood. This was primarily due to the difficulty of instrumenting monitoring devices at individual homes at a large scale. The recent boom of Internet TV (IPTV) has enabled us to monitor the user behavior and network usage of an entire network. Such analysis can provide a clearer picture of how people watch TV and how the underlying networks and systems can better adapt to future challenges. In this paper, we present the first analysis of IPTV workloads based on network traces from one of the world's largest IPTV systems. Our dataset captures the channel change activities of 250,000 households over a six month period. We characterize the properties of viewing sessions, channel popularity dynamics, geographical locality, and channel switching behaviors. We discuss implications of our findings on networks and systems, including the support needed for fast channel changes. Our data analysis of an operational IPTV system has important implications on not only existing and future IPTV systems, but also the design of the open Internet TV distribution systems such as Joost and BBC's iPlayer that distribute television on the wider Internet.
Companion of the 17th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications - OOPSLA '02, 2002
... es Pau Arumi Music Technology Group Pompeu Fabra University Barcelona, Spain [email protected]... more ... es Pau Arumi Music Technology Group Pompeu Fabra University Barcelona, Spain [email protected] Miguel Ramirez Music Technology Group Pompeu Fabra University Barcelona, Spain [email protected] ABSTRACT ...
Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA '06, 2006
CLAM is a C++ framework that offers a complete development and research platform for the audio an... more CLAM is a C++ framework that offers a complete development and research platform for the audio and music domain. Apart from offering an abstract model for audio systems, it also includes a repository of processing algorithms and data types as well as a number of tools such as audio or MIDI input/output. All these features can be exploited to build cross-platform applications or to build rapid prototypes to test signal and media processing algorithms and systems. The framework also includes a number of stand-alone applications that can be used for tasks such as audio analysis/synthesis, plug-in development or metadata annotation.
Proceedings of the 2006 conference on Pattern languages of programs - PLoP '06, 2006
This article describes a set of patterns the authors have seen emerging during years of experienc... more This article describes a set of patterns the authors have seen emerging during years of experience developing assorted applications in the sound and music domain and receiving influences from theoretical models, existing systems, and colleagues.
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Papers by Xavier Amatriain