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Joe Corneli draft of recommender systems stuf about 9 years ago

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@inproceedings{abbassi2009getting, title={Getting recommender systems to think outside the box}, author={Abbassi, Zeinab and Amer-Yahia, Sihem and Lakshmanan, Laks VS and Vassilvitskii, Sergei and Yu, Cong}, booktitle={Proceedings of the third ACM conference on Recommender systems}, pages={285--288}, year={2009}, organization={ACM} } @inproceedings{mendoncca2008unsought, title={Unsought innovation: serendipity in organizations}, author={Mendon{\c{c}}a, Sandro and Cunha, Miguel Pina E and Clegg, Stewart R},

} @phdthesis{shengbo-guo-thesis, title={{B}ayesian {R}ecommender {S}ystems: {M}odels and {A}lgorithms}, author={Guo, Shengbo}, year={2011}, school={The Australian National University}} @phdthesis{eric-nichols-thesis, title={{M}usicat: {A} {C}omputer {M}odel of {M}usical {L}istening and {A}nalogy-{M}aking}, author={Eric Paul Nichols},

Title = {Gum-Elastic and its Varieties, with a Detailed Account of its Applications and Uses, and of the Discovery of Vulcanization}, Year = {1855}} @article{tce-postits, Author = {Flavell-While, Claudia}, Journal = {The Chemical Engineer}, Month = {August}, Pages = {53--55}, Title = {{S}pencer {S}ilver and {A}rthur {F}ry: the chemist and the tinkerer who created the {P}ost-it {N}ote}, Year = {2012}} @incollection{bex-generalising, Author = {Bex, Floris and Lawrence, John and Reed, Chris}, Booktitle = {Fifth International Conference on Computational Models of Argument},

Bdsk-Url-1 = {http://www.emeraldinsight.com/10.1108/00220411211256030}, Bdsk-Url-2 = {http://dx.doi.org/10.1108/00220411211256030}} @incollection{iaquinta2010can, title={Can a recommender system induce serendipitous encounters?}, author={Iaquinta, Leo and Semeraro, Giovanni and de Gemmis, Marco and Lops, Pasquale and Molino, Piero}, booktitle={E-commerce}, editor={Kang, Kyeong}, year={2010}, publisher={InTech} } @article{Iaquinta2008, Author = {Iaquinta, L. and Gemmis, M. and Lops, P. and Semeraro, G. and Filannino, M. and Molino, P.}, Doi = {10.1109/HIS.2008.25},

\subsection{Prior partial examples} \paragraph{{[}To add: Jazz.{]}} \citeA{pease2013discussion} used a somewhat different version of the SPECS criteria to discuss three examples, related to dynamic investigation problems, model generation, and poetry flowcharts. The \paragraph{{[}To add: HR.{]}} Jazz.{]}} \paragraph{Recommender systems.} Research in recommender systems focusses on stimulating serendipity on the user side, by suggesting items that might more be unexpected, novel, and valuable 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. \paragraph{{[}To add: HR.{]}} A recommender system must come with a \textbf{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 \textbf{result} which is unexpected, novel, and valuable to a specific user. % while the user's goal is to find items that are valuable in different respects. \paragraph{Recommender systems.} Related work tries to structure As discussed in Section \ref{sec:related}, recommender systems are one of the search space and exploit patterns as \textbf{serendipity triggers}. For example, \cite{Herlocker2004} as well as \cite{Lu2012} associate less popular items with primary contexts in computing where serendipity is seen to play a higher unexpectedness. Clustering was role. As we noted, these systems mostly focus on discovery. Nevertheless, certain architectures that also frequently used to discover latent structures in the search space. For example, \cite{Kamahara2005} partition users into clusters take account of common interest, while \cite{Onuma2009} as well as \cite{Zhang2011} perform clustering on both users and items. In invention may match the work criteria described by \cite{Oku2011}, our model. We draw on the user is allowed observation that recommender systems not only aim to select two items in order to mix their features in some sort \emph{stimulate} serendipitous discovery for the user: they also have the task of conceptual blending. \emph{simulating} when this is likely to occur. If a pattern is found, it A recommendation is used typically provided if the system suspects that the item will be likely to \textbf{bridge} between items introduce ideas that are known and valuable close to what the user, and those user knows, but that are potentially will be unexpected. As an In other words, the system aims to stimulate serendipity for the user. For example, \cite{Sugiyama2011} connects users with divergent interests, while \cite{Onuma2009} weight items stronger a museum recommender service might suggest a colourful medieval painting to a user who seems to like colourful paintings by the modern artist Keith Haring. User behaviour (e.g.~following up on these recommendations) is outside of the direct control of the system and may serve as a \textbf{serendipity trigger}, and change the way it makes recommendations in the future. The system has a \textbf{prepared mind}, including both a \emph{user model} and a \emph{domain model}, both of which can be updated dynamically. The connections through which recommendations are made usually happen when the system notices that elements of the domain have something in common via clustering or faceting. A \textbf{bridge} to a new kind of recommendation may be found if new elements are introduced into the domain which do not cluster well, or if the user appears to know about different clusters that bridge do not have obvious connections between topical clusters. them. The intended outcome of recommendations depend on the organisational mission e.g.~to make money, to provide a good user experience, etc.; at the system level, the serendiptious \textbf{result} would be learning a new approach that helps to address these goals better. Recommender systems From the perspective of our model, \textbf{chance} will only haveto cope with a \textbf{dynamic world} of significant role if the system has the capacity to learn from user preferences and new items that behaviour. Indeed, Bayesian methods are introduced used in contemporary recommender systems (surveyed in Chapter 3 of \citeNP{shengbo-guo-thesis}). Combined with the ability to learn, \textbf{curiosity} could be described as the system. The imperfect knowledge urge to make ``outside-the-box''\footnote{\citeA{abbassi2009getting}.} recommendations specifically for the purposes of learning more about users. The typical commercial perspective on recommendations is related to the user's preferences process of ``conversion'' -- turning recommendations into clicks and interests represents perhaps the strongest dimension clicks into purchases. Measures of \textbf{chance}. Determining \textbf{sagacity} would relate to the system's ability to draw inferences from user behaviour to update the recommendation model. For example, the system might do A/B testing to decide how novel recommendation strategies influences conversion. The \textbf{value} of an item, both new recommendation strategies can be measured in terms of value for the user and unexpectedness, is of paramount importance. traditional business metrics or other organisational objectives.

An active research community investigating computational models of serendipity exists in the field of information retrieval, and specifically, in recommender systems \cite{Toms2000}. In this domain, \citeA{Herlocker2004} and \citeA{McNee2006} view serendipity as an important factor for user satisfaction, next to accuracy and diversity. Serendipity in recommendations variously require the system to deliver an \emph{unexpected} and \emph{useful} \cite{Lu2012}, \emph{interesting} \cite{Herlocker2004}, \emph{attractive} or \emph{relevant} item \cite{Ge2010}. %% Recommendations are typically meant to help address the user's difficulty in finding items that meet his or her interests or demands within a large and potentially unobservable search space. The end user can also be passive, and items are suggested to support other stakeholder's goals, e.g. to increase sells. Definitions differ as to the requirement of \emph{novelty}; \citeA{Adamopoulos2011}, for example, describe systems that suggest items that may already be known, but are still unexpected in the current context. While standardized measures such as the $F_1$-score or the (R)MSE are used to determine the accuracy \emph{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, although there are several proposals \cite{Murakami2008, Adamopoulos2011, McCay-Peet2011}. McCay-Peet2011,iaquinta2010can}. In terms of our model, these systems focus mainly on producing a \emph{serendipity trigger} for the user, but they include aspects of user modeling which could bring other elements into play. Paul Andr{\'e} et al.~\citeyear{andre2009discovery} have examined