Recommender systems: from algorithms to user experience.
User Modeling and User-Adapted Interaction, 22(1-2):101-123, 2012.
JosephA. Konstan and John Riedl.
[doi]
[abstract]
[BibTeX]
Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
Data structures and algorithms in Java.
2011.
Michael T. Goodrich and Roberto Tamassia.
[doi]
[BibTeX]
Experimental study on the five sort algorithms..
2011.
Ping Yu und Yan Can Yang, You.
[BibTeX]
Two Basic Algorithms in Concept Analysis.
In:
L. Kwuida and B. Sertkaya, editors,
Formal Concept Analysis, pages 312-340.
Springer, Berlin / Heidelberg, 2010.
Bernhard Ganter.
[doi]
[BibTeX]
Empirical Comparison of Algorithms for Network Community Detection.
2010. cite arxiv:1004.3539
.
Jure Leskovec, Kevin J. Lang and Michael W. Mahoney.
[doi]
[abstract]
[BibTeX]
Detecting clusters or communities in large real-world graphs such as large
social or information networks is a problem of considerable interest. In
practice, one typically chooses an objective function that captures the
intuition of a network cluster as set of nodes with better internal
connectivity than external connectivity, and then one applies approximation
algorithms or heuristics to extract sets of nodes that are related to the
objective function and that "look like" good communities for the application of
interest. In this paper, we explore a range of network community detection
methods in order to compare them and to understand their relative performance
and the systematic biases in the clusters they identify. We evaluate several
common objective functions that are used to formalize the notion of a network
community, and we examine several different classes of approximation algorithms
that aim to optimize such objective functions. In addition, rather than simply
fixing an objective and asking for an approximation to the best cluster of any
size, we consider a size-resolved version of the optimization problem.
Considering community quality as a function of its size provides a much finer
lens with which to examine community detection algorithms, since objective
functions and approximation algorithms often have non-obvious size-dependent
behavior.
Introduction to algorithms.
2009.
Thomas H. Cormen.
[doi]
[BibTeX]
Introduction to algorithms.
2009.
Thomas H. Cormen.
[doi]
[BibTeX]
Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike.
In:
Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web, volume 467, series CEUR Workshop Proceedings.
2009.
Denis Parra and Peter Brusilovsky.
[doi]
[abstract]
[BibTeX]
Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system.
Sorting the slow way: an analysis of perversely awful randomized sorting algorithms.
In:
Proceedings of the 4th international conference on Fun with algorithms, series FUN'07, pages 183-197.
Springer-Verlag, Berlin, Heidelberg, 2007.
Hermann Gruber, Markus Holzer and Oliver Ruepp.
[doi]
[abstract]
[BibTeX]
This paper is devoted to the "Discovery of Slowness." The archetypical perversely awful algorithm bogo-sort, which is sometimes referred to as Monkey-sort, is analyzed with elementary methods. Moreover, practical experiments are performed.
Prediction and ranking algorithms for event-based network data.
SIGKDD Explor. Newsl., 7(2):23-30, 2005.
Joshua O'Madadhain, Jon Hutchins and Padhraic Smyth.
[doi]
[abstract]
[BibTeX]
Event-based network data consists of sets of events over time, each of which may involve multiple entities. Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). Traditional network analysis techniques, such as social network models, often aggregate the relational information from each event into a single static network. In contrast, in this paper we focus on the temporal nature of such data. In particular, we look at the problems of temporal link prediction and node ranking, and describe new methods that illustrate opportunities for data mining and machine learning techniques in this context. Experimental results are discussed for a large set of co-authorship events measured over multiple years, and a large corporate email data set spanning 21 months.
Lanczos algorithms for large symmetric eigenvalue computations: Documentaion and Listings Original Lanczos Codes.
2002.
J.K. Cullum and R.A. Willoughby.
[doi]
[BibTeX]
Lanczos algorithms for large symmetric eigenvalue computations: Theory.
2002.
J.K. Cullum and R.A. Willoughby.
[doi]
[BibTeX]
Comparing performance of algorithms for generating concept lattices.
Journal of Experimental & Theoretical Artificial Intelligence, 14(2-3):189-216, 2002.
Sergei O. Kuznetsov and Sergei A. Obiedkov.
[doi]
[BibTeX]
The art of computer programming : 1. Fundamental algorithms.
1997.
Donald Ervin Knuth.
[doi]
[BibTeX]
The art of computer programming : 1. Fundamental algorithms.
1997.
Donald Ervin Knuth.
[doi]
[BibTeX]
The art of computer programming : 1. Fundamental algorithms.
1997.
Donald Ervin Knuth.
[doi]
[BibTeX]
The art of computer programming : 1. Fundamental algorithms.
1997.
Donald Ervin Knuth.
[doi]
[BibTeX]
The art of computer programming : 1. Fundamental algorithms.
1997.
Donald Ervin Knuth.
[doi]
[BibTeX]
Introspective sorting and selection algorithms.
Software — Practice and Experience, 27(8):983 - 993 , 1997.
David R. Musser.
[BibTeX]
Solving Least Squares Problems (Classics in Applied Mathematics).
1987.
Charles L. Lawson and Richard J. Hanson.
[doi]
[BibTeX]