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27
A Comprehensive Survey of Neighborhoodbased Recommendation Methods.
 In Recommender Systems Handbook,
, 2011
"... Abstract Among collaborative recommendation approaches, methods based on nearestneighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neig ..."
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Abstract Among collaborative recommendation approaches, methods based on nearestneighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhoodbased methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhoodbased recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.
A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph
 In Proceedings of the 6th Workshop on SelfOrganizing Maps (WSOM 07
, 2007
"... Abstract — Flexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version ..."
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Cited by 25 (8 self)
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Abstract — Flexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version of the SOM algorithm to data described by a dissimilarity measure, the various versions of the second ones are stochastic SOM. We propose here to introduce a batch version of the kernel SOM and to show how this one is related to the dissimilarity SOM. Finally, an application to the classification of the vertices of a graph is proposed and the algorithms are tested and compared on a simulated data set. 1
A family of dissimilarity measures between nodes generalizing both the shortestpath and the commutetime distances
 in Proceedings of the 14th SIGKDD International Conference on Knowledge Discovery and Data Mining
"... This work introduces a new family of linkbased dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortestpath (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortestpath d ..."
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Cited by 24 (11 self)
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This work introduces a new family of linkbased dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortestpath (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortestpath distance when θ is large and, on the other end, to the commutetime (or resistance) distance when θ is small (near zero). Intuitively, it corresponds to the expected cost incurred by a random walker in order to reach a destination node from a starting node while maintaining a constant entropy (related to θ) spread in the graph. The parameter θ is therefore biasing gradually the simple random walk on the graph towards the shortestpath policy. By adopting a statistical physics approach and computing a sum over all the possible paths (discrete path integral), it is shown that the RSP dissimilarity from every node to a particular node of interest can be computed efficiently by solving two linear systems of n equations, where n is the number of nodes. On the other hand, the dissimilarity between every couple of nodes is obtained by inverting an n × n matrix. The proposed measure can be used for various graph mining tasks such as computing betweenness centrality, finding dense communities, etc, as shown in the experimental section.
Graph nodes clustering with the sigmoid commutetime kernel: A . . .
 DATA & KNOWLEDGE ENGINEERING
, 2009
"... ..."
An experimental investigation of kernels on graphs for collaborative . . .
 NEURAL NETWORKS
, 2012
"... ..."
WithinNetwork Classification Using Local Structure Similarity
 European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2009, Bled, Slovenia), LNAI 5781:260–275
, 2009
"... Abstract. Withinnetwork classification, where the goal is to classify the nodes of a partly labeled network, is a semisupervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. Wh ..."
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Abstract. Withinnetwork classification, where the goal is to classify the nodes of a partly labeled network, is a semisupervised learning problem that has applications in several important domains like image processing, the classification of documents, and the detection of malicious activities. While most methods for this problem infer the missing labels collectively based on the hypothesis that linked or nearby nodes are likely to have the same labels, there are many types of networks for which this assumption fails, e.g., molecular graphs, trading networks, etc. In this paper, we present a collective classification method, based on relaxation labeling, that classifies entities of a network using their local structure. This method uses a marginalized similarity kernel that compares the local structure of two nodes with parallel random walks in the network. Through experimentation on different datasets, we show our method to be more accurate than several stateoftheart approaches for this problem. Key words: Network, semisupervised learning, random walk 1
A LinkAnalysis Extension of Correspondence Analysis for Mining Relational Databases
"... Abstract—This work introduces a linkanalysis procedure for discovering relationships in a relational database or a graph, generalizing both simple and multiple correspondence analysis. It is based on a randomwalk model through the database defining a Markov chain having as many states as elements ..."
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Abstract—This work introduces a linkanalysis procedure for discovering relationships in a relational database or a graph, generalizing both simple and multiple correspondence analysis. It is based on a randomwalk model through the database defining a Markov chain having as many states as elements in the database. Suppose we are interested in analyzing the relationships between some elements (or records) contained in two different tables of the relational database. To this end, in a first step, a reduced, much smaller, Markov chain containing only the elements of interest and preserving the main characteristics of the initial chain, is extracted by stochastic complementation [41]. This reduced chain is then analyzed by projecting jointly the elements of interest in the diffusionmap subspace [42] and visualizing the results. This twostep procedure reduces to simple correspondence analysis when only two tables are defined and to multiple correspondence analysis when the database takes the form of a simple starschema. On the other hand, a kernel version of the diffusionmap distance, generalizing the basic diffusionmap distance to directed graphs, is also introduced and the links with spectral clustering are discussed. Several datasets are analyzed by using the proposed methodology, showing the usefulness of the technique for extracting relationships in relational databases or graphs. I.
Alternative Similarity Functions for Graph Kernels
"... Given a bipartite graph of collaborative ratings, the task of recommendation and rating prediction can be modeled with graph kernels. We interpret these graph kernels as the inverted squared Euclidean distance in a space defined by the underlying graph and show that this inverted squared Euclidean s ..."
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Given a bipartite graph of collaborative ratings, the task of recommendation and rating prediction can be modeled with graph kernels. We interpret these graph kernels as the inverted squared Euclidean distance in a space defined by the underlying graph and show that this inverted squared Euclidean similarity function can be replaced by other similarity functions. We evaluate several such similarity functions in the context of collaborative item recommendation and rating prediction, using the exponential diffusion kernel, the von Neumann kernel, and the random forest kernel as a basis. We find that the performance of graph kernels for these tasks can be increased by using these alternative similarity functions. 1.
Evaluating performance of recommender systems: An experimental comparison
"... Much early evaluation work focused specifically on the “accuracy ” of recommendation algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with other considerations. This work suggests measures aiming at evaluating other aspects than accuracy of recommendation algorithms ..."
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Much early evaluation work focused specifically on the “accuracy ” of recommendation algorithms. Good recommendation (in terms of accuracy) has, however, to be coupled with other considerations. This work suggests measures aiming at evaluating other aspects than accuracy of recommendation algorithms. Other considerations include (1) coverage, which measures the percentage of a data set that a recommender system is able to provide recommendation for, (2) confidence metrics that can help users make more effective decisions, (3) computing time, which measures how quickly an algorithm can produce good recommendations, (4) novelty/serendipity, which measure whether a recommendation is original, and (5) robustness which measure the ability of the algorithm to make good predictions in the presence of noisy or sparse data. Six collaborative recommendation methods are investigated. Results on artificial data sets (for robustness) or on the real MovieLens data set (for accuracy, novelty, and computing time) are included and analyzed, showing that kernelbased algorithms provide the best results overall. 1
Fast and Accurate Link Prediction in Social Networking Systems
, 2012
"... Online social networks (OSNs) recommend new friends to registered users based on localbased features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maxi ..."
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Online social networks (OSNs) recommend new friends to registered users based on localbased features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are globalbased approaches, which detect the overall path structure in a network, being computationally prohibitive for hugesized social networks. In this paper we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a limited length, based on the “algorithmic small world hypothesis”. As a result, we are able to provide more accurate and faster friend recommendations. We also derive variants of our method that apply to different types of networks (directed/undirected and signed/unsigned). We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using synthetic and three real data sets (Epinions, Facebook and Hi5). We also show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability.