view Report/chiliguano_msc_finalproject.bib @ 22:ce1e76c21c12

Removed library for matrix factorization

@inbook{Lops11,
        author={Lops,Pasquale and de Gemmis,Marco and Semeraro,Giovanni},
        year={2011},
        title={Content-based Recommender Systems: State of the Art and Trends},
        publisher={Springer US},
        address={Boston, MA},
        pages={73-105},
        abstract={Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.},
        keywords={Computer Science; Artificial Intelligence (incl. Robotics); User Interfaces and Human Computer Interaction; Information Storage and Retrieval; e-Commerce/e-business; Database Management; Data Mining and Knowledge Discovery},
        isbn={0387858199; 9780387858197},
        language={English},
        url={http://qmul.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3PS8MwFA7iyR-gTsX5A3LRi1TTpl2doDDG5g56suIxZEsDA9vN2f3_5iVpWlcRdvO2Dl6b5it5L--97wtCNLgh3sqaEAqeylRFr34ALpRPIkK45Bzk0GkgIf3xntD-MBwkwXNV0K_---fAr0TEcHkZkCr9aqVj1b0yfUJcqUd-PeK5cMbQfjObW4H-L2BVOnAF6FBnmdEdeFFTMXMZGE0kNqSYJ2hgzfNp_XPTKld54YFXBMZjYwyQetCRbdmV0FuY5nbTlVvPPmgOXj37UGYfnV6i3ZhCUfwuUtFGXFsczZkl1s36mm3dXMGrpg2j-6vuAhxvRiuHVRbpV_yY6y5sGoNoWidmQOmgV6CpnonppHhIc-_tVblvPyBw-EVAei49B3XumHZsfV-_StfqNblXcyJWdZ1iN95GaV1HLMk-2gEWC4axqEEfoI00b6G98gwPbJf0FtquCVIeoscfMOIajNjCeI81iHgmsQIRKxCxAhEbEI9QZzhI-iOvHBKbGzET9vdk0WO0y4FqkReakilOEKah2gaIDvdFFIdjtQOQsht2JRE8GquQKGqj2zWf0kbkNwtGmLJg2oJRZmdM_ZoLebr2Q87QVvUBn6PNYrFML9DmZ7b8-Abh729j},
}
}

@article{Burke02,
        author={Burke,R.},
        year={2002},
        title={Hybrid recommender systems: Survey and experiments},
        journal={User Modelling and User-Adapted Interaction},
        volume={12},
        number={4},
        pages={331-370},
        url={www.scopus.com},
}
}

@article{Yoshii08,
        author={Yoshii,K. and Goto,M. and Komatani,K. and Ogata,T. and Okuno,H. G.},
        year={2008},
        title={An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model},
        journal={IEEE Transactions on Audio, Speech and Language Processing},
        volume={16},
        number={2},
        pages={435-447},
        url={www.scopus.com},
}
}

@article {JCC4:JCC4393,
author = {boyd, danah m. and Ellison, Nicole B.},
title = {Social Network Sites: Definition, History, and Scholarship},
journal = {Journal of Computer-Mediated Communication},
volume = {13},
number = {1},
publisher = {Blackwell Publishing Inc},
issn = {1083-6101},
url = {http://dx.doi.org/10.1111/j.1083-6101.2007.00393.x},
doi = {10.1111/j.1083-6101.2007.00393.x},
pages = {210--230},
year = {2007},
}

@article{Castanedo13,
        author={Castanedo,F.},
        year={2013},
        title={A review of data fusion techniques},
        journal={The Scientific World Journal},
        volume={2013},
        url={www.scopus.com},
}
}

@inproceedings{Lee09,
        author={Lee,H. and Yan,L. and Pham,P. and Ng,A. Y.},
        editor={ },
        year={2009},
        title={Unsupervised feature learning for audio classification using convolutional deep belief networks},
        booktitle={Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference},
        pages={1096-1104},
        url={www.scopus.com},
}
}

@TechReport {export:115396,
abstract     = {<p>Recommender systems are now popular both commercially and in the research
                community, where many approaches have been suggested for providing
                recommendations. In many cases a system designer that wishes to employ a
                recommendation system must choose between a set of candidate approaches.  A first
                step towards selecting an appropriate algorithm is to decide which properties of
                the application to focus upon when making this choice.  Indeed, recommendation
                systems have a variety of properties that may affect user experience, such as
                accuracy, robustness, scalability, and so forth. In this paper we discuss how to
                compare recommenders based on a set of properties that are relevant for e
                application. We focus on comparative studies, where a few algorithms are compared
                using some evaluation metric, rather than absolute benchmarking of algorithms. We
                describe experimental settings appropriate for making choices between algorithms.
                We review three types of experiments, starting with an offline setting, where
                recommendation approaches are compared without user interaction, then reviewing
                user studies, where a small group of subjects experiment with the system and
                report on the experience, and finally describe large scale online experiments,
                where real user populations interact with the system. In each of these cases we
                describe types of questions that can be answered, and suggest protocols for
                experimentation. We also discuss how to draw trustworthy conclusions from e
                conducted experiments. We then review a large set of properties, and explain how
                to evaluate systems given relevant properties. We also survey a large set of
                evaluation metrics in the context of the property that they evaluate.</p>},
author       = {Guy Shani and Asela Gunawardana},
month        = {November},
number       = {MSR-TR-2009-159},
publisher    = {Microsoft Research},
title        = {Evaluating Recommender Systems},
url          = {http://research.microsoft.com/apps/pubs/default.aspx?id=115396},
year         = {2009},
}

@phdthesis {1242,
	title = {Music Recommendation and Discovery in the Long Tail},
	year = {2008},
	school = {Universitat Pompeu Fabra},
	address = {Barcelona},
	abstract = {<p class="small">
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1\% of
all digital tracks accounted for 80\% of all sales. Similarly, 1,000 albums accounted for 50\%
of all album sales, and 80\% of all albums sold were purchased less than 100 times. There is
a need to assist people to filter, discover, personalise and recommend from the huge amount
of music content available along the Long Tail.
</p>
<p class="small">
Current music recommendation algorithms try to
accurately predict what people demand to listen to. However, quite
often these algorithms tend to recommend popular -or well-known to the
user- music, decreasing the effectiveness of the recommendations. These
approaches focus on improving the accuracy of the recommendations. That
is, try to make
accurate predictions about what a user could listen to, or buy next,
independently of how
useful to the user could be the provided recommendations.
</p>
<p class="small">
In this Thesis we stress the importance of the user{\textquoteright}s
perceived quality of the recommendations. We model the Long Tail curve
of artist popularity to predict -potentially-
interesting and unknown music, hidden in the tail of the popularity
curve. Effective recommendation systems should promote novel and
relevant material (non-obvious recommendations), taken primarily from
the tail of a popularity distribution.
</p>
<p class="small">
The main contributions of this Thesis are: <em>(i)</em> a novel network-based approach for
recommender systems, based on the analysis of the item (or user) similarity graph, and the
popularity of the items, <em>(ii)</em> a user-centric evaluation that measures the user{\textquoteright}s relevance
and novelty of the recommendations, and <em>(iii)</em> two prototype systems that implement the
ideas derived from the theoretical work. Our findings have significant implications for
recommender systems that assist users to explore the Long Tail, digging for content they
might like.
</p>
},
	url = {static/media/PhD_ocelma.pdf},
	author = {Celma, {\`O}.}
}