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year = {2013}  }  @inproceedings{Kamahara2005,  author = {Kamahara, Junzo and Asakawa, T},  booktitle = {Proceedings of the 11th Internationall Multimedia Modelling Conference},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Kamahara, Asakawa - 2005 - A community-based recommendation system to reveal unexpected interests.pdf:pdf},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  pages = {433--438},  title = {{A community-based recommendation system to reveal unexpected interests}},  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1386026},  year = {2005}  }  @inproceedings{Onuma2009,  author = {Onuma, Kensuke and Tong, H and Faloutsos, Christos},  booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Onuma, Tong, Faloutsos - 2009 - TANGENT A Novel 'Surprise-me' Recommendation Algorithm.pdf:pdf},  isbn = {9781605584959},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  title = {{TANGENT : A Novel 'Surprise-me' Recommendation Algorithm}},  url = {http://dl.acm.org/citation.cfm?id=1557093},  year = {2009}  }  @inproceedings{Oku2011,  author = {Oku, Kenta and Hattori, F},  booktitle = {Proceedings of the Workshop on Novelty and Diversity in Recommender Systems (DiveRS 2011), at the 5th ACM International Conference on Recommender Systems},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Oku, Hattori - 2011 - Fusion-based recommender system for improving serendipity.pdf:pdf},  keywords = {fusion-based recommender system,recommender system},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  number = {DiveRS},  title = {{Fusion-based recommender system for improving serendipity}},  url = {http://ceur-ws.org/Vol-816/divers2011.pdf\#page=29},  year = {2011}  }  @inproceedings{Zhang2011,  author = {Zhang, Yuan Cao and S\'{e}aghdha, Diarmuid \'{O} and Quercia, Daniele and Jambor, Tamas},  booktitle = {Proceedings of the fifth ACM international conference on Web search and data mining},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Zhang et al. - 2011 - Auralist Introducing Serendipity into Music Recommendation.pdf:pdf},  keywords = {accuracy,collaborative filtering,diversification,metrics,novelty,recommender systems,serendipity},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  pages = {13--22},  title = {{Auralist : Introducing Serendipity into Music Recommendation}},  year = {2011}  }  @inproceedings{Sugiyama2011,  author = {Sugiyama, Kazunari},  booktitle = {Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Sugiyama - 2011 - Serendipitous Recommendation for Scholarly Papers.pdf:pdf},  isbn = {9781450307444},  keywords = {recommendation,serendipity,user modeling},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  pages = {307--310},  title = {{Serendipitous Recommendation for Scholarly Papers}},  year = {2011}  }  @inproceedings{Onuma2009,  author = {Onuma, Kensuke and Tong, H and Faloutsos, Christos},  booktitle = {Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Onuma, Tong, Faloutsos - 2009 - TANGENT A Novel 'Surprise-me' Recommendation Algorithm.pdf:pdf},  isbn = {9781605584959},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  title = {{TANGENT : A Novel 'Surprise-me' Recommendation Algorithm}},  url = {http://dl.acm.org/citation.cfm?id=1557093},  year = {2009}  }  @inproceedings{McCay-Peet2011,  author = {McCay-Peet, Lori and Toms, Elaine G.},  booktitle = {Proceedings of the American Society for Information Science and Technology},  file = {:Users/worldwindow/Documents/Mendeley Desktop/McCay-Peet, Toms - 2011 - The serendipity quotient.pdf:pdf},  mendeley-groups = {Magisterarbeit/Serendipity},  pages = {1--4},  title = {{The serendipity quotient}},  url = {http://onlinelibrary.wiley.com/doi/10.1002/meet.2011.14504801236/full},  year = {2011}  }  @incollection{Murakami2008,  author = {Murakami, Tomoko and Mori, Koichiro and Orihara, Ryohei},  booktitle = {New Frontiers in Artificial Intelligence},  file = {:Users/worldwindow/Documents/Mendeley Desktop/Murakami, Mori, Orihara - 2008 - Metrics for evaluating the serendipity of recommendation lists.pdf:pdf},  mendeley-groups = {Magisterarbeit/Recommendation and Serendipity},  pages = {40--46},  title = {{Metrics for evaluating the serendipity of recommendation lists}},  url = {http://link.springer.com/chapter/10.1007/978-3-540-78197-4\_5},  year = {2008}  }  diff --git a/cc-intro.tex b/cc-intro.tex  index 987c101..0df8e6c 100644  --- a/cc-intro.tex  +++ b/cc-intro.tex 
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\subsection{Prior partial examples}  \textbf{[Jazz, recommender systems, HR.]}  Research in recommender systems focusses on stimulating serendipity on the user side, by suggesting items that might more likely be unexpected, potentially novel, and necessarily of value to the user. Several components of the proposed model were implemented in related work, and art and music recommendation connects to the area of computational creativity.  A recommender system must come with a prepared mind in terms of full knowledge of the items in the search space, and of the user's partial knowledge of their existence and properties. Predictions are based on existing knowledge of the items known to the user, and his or her preferences. The system's goal is to recommend an item as result which is unexpected, valuable and potentially novel to a specific user, while the user's goal is to find items that are valuable in different respects.   Related work tries to structure the search space and exploit patterns as serendipity triggers. For example, \cite{Herlocker2004} as well as \cite{Lu2012} associate less popular items with a higher unexpectedness. Clustering was also frequently used to discover latent structures in the search space. For example, \cite{Kamahara2005} partition users into clusters of common interest, while \cite{Onuma2009} as well as \cite{Zhang2011} perform clustering on both users and items. In the work by \cite{Oku2011}, the user is allowed to select two items in order to mix their features in some sort of conceptual blending.   If a pattern is found, it is used to bridge between items that are known and valuable to the user, and those that are potentially unexpected. As an example, \cite{Sugiyama2011} connects users with divergent interests, while \cite{Onuma2009} weight items stronger that bridge between topical clusters.   Recommender systems have to cope with a dynamic world of user preferences and new items that are introduced to the system. The imperfect knowledge about the user's preferences and interests represents perhaps the strongest dimension of chance. Determining the value of an item, both in terms of value for the user and unexpectedness, is of paramount importance. While standardized measures such as the $F_1$-score or the (R)MSE )are used to determine the accuracy of an evaluation in terms of preferred items in the user's history, there is no common agreement on a measure for serendipity yet \cite{ Murakami2008, Adamopoulos2011, McCay-Peet2011}.  diff --git a/serendipity.pdf b/serendipity.pdf  index 824d845..18b0146 100644  Binary files a/serendipity.pdf and b/serendipity.pdf differ