CiteULike: koles's library [174 articles]

A comparison of document clustering techniques

M Steinbach — 2008-03-28T23:54:02-00:00

(2000)

This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a "standard" K-means algorithm and a variant of K-means, "bisecting" K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and...

Follow the reader: filtering comments on slashdot

Cliff Lampe — 2007-12-18T19:35:55-00:00

(2007), pp. 1253-1262.

Beyond Recommender Systems: Helping People Help Each Other

L Terveen — 2007-11-01T10:19:54-00:00

(2001)

The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other's...

Survey Of Clustering Data Mining Techniques

Pavel Berkhin — 2006-03-20T05:34:41-00:00

(2002)

Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters neccessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historial perspective rooted in mathematics, statistics and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters in unsupervised learning and the resulting system represents a data concept....

Modern Information Retrieval: A Brief Overview

Amit Singhal — 2007-10-04T09:09:30-00:00

Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, Vol. 24, No. 4. (2001), pp. 35-42.

Using New Soft Clustering Technique in Adaptive Web Site

RA Shokry — 2008-02-25T20:13:42-00:00

Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on (2006), pp. 281-286.

The ability of the Web site to automatically reorganize the site structure towards the benefit of user groups or individuals, known as Web site adaptation, is becoming a major requirement for Web site designers to improve their sites' usability. Clustering techniques are used as the basis of many existing approaches for developing adaptive Web sites. However the used clustering techniques limit the efficiency of these approaches. We developed a new soft clustering technique to be used as the underlying technique for adaptive Web sites. In particular, it proposes the use of soft k-medoids technique to provide a more efficient solution to the index page synthesis problem. The performance of the proposed systems is evaluated using real data. The performance evaluation experiments showed the effectiveness of the proposed systems. Also, the experiments showed the scalability of the proposed systems for an increasing number of users and for an increasing size of Web logs

Web mining: a survey in the fuzzy framework

Dragos Arotaritei — 2008-01-31T01:34:43-00:00

Fuzzy Sets and Systems, Vol. 148, No. 1. (16 November 2004), pp. 5-19.

This article provides a survey of the available literature on fuzzy Web mining. The different aspects of Web mining, like clustering, association rule mining, navigation, personalization, Semantic Web, information retrieval, text and image mining are considered under the existing taxonomy. The role of fuzzy sets in handling the different types of uncertainties/impreciseness is highlighted. A hybridization of fuzzy sets with genetic algorithms (another soft computing tool) is described for information retrieval. An extensive bibliography is also included.

Web mining in soft computing framework: relevance, state of the art and future directions

SK Pal — 2008-01-31T01:31:12-00:00

Neural Networks, IEEE Transactions on, Vol. 13, No. 5. (2002), pp. 1163-1177.

The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) are highlighted. A survey of the existing literature on "soft Web mining" is provided along with the commercially available systems. The prospective areas of Web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft Web mining" systems is explained. An extensive bibliography is also provided.

Web mining with relational clustering

TA Runkler — 2008-01-31T01:28:15-00:00

International Journal of Approximate Reasoning, Vol. 32, No. 2-3. (February 2003), pp. 217-236.

Clustering is an unsupervised learning method that determines partitions and (possibly) prototypes from pattern sets. Sets of numerical patterns can be clustered by alternating optimization (AO) of clustering objective functions or by alternating cluster estimation (ACE). Sets of non-numerical patterns can often be represented numerically by (pairwise) relations. These relational data sets can be clustered by relational AO and by relational ACE (RACE). We consider two kinds of non-numerical patterns provided by the World Wide Web: document contents such as the text parts of web pages, and sequences of web pages visited by particular users, so-called web logs. The analysis of document contents is often called web content mining, and the analysis of log files with web page sequences is called web log mining. For both non-numerical pattern types (text and web page sequences) relational data sets can be automatically generated using the Levenshtein (edit) distance or using graph distances. The prototypes found for text data can be interpreted as keywords that serve for document classification and automatic archiving. The prototypes found for web page sequences can be interpreted as prototypical click streams that indicate typical user interests, and therefore serve as a basis for web content and web structure management.

Editorial: special issue on web content mining

Bing Liu — 2008-01-25T16:22:41-00:00

SIGKDD Explor. Newsl., Vol. 6, No. 2. (December 2004), pp. 1-4.

Web Usage Mining as a Tool for Personalization: A Survey

Dimitrios Pierrakos — 2006-01-18T22:23:40-00:00

User Modeling and User-Adapted Interaction, Vol. 13, No. 4. (November 2003), pp. 311-372.

Using data mining as a strategy for assessing asynchronous discussion forums

Laurie Dringus — 2008-01-25T13:25:07-00:00

Computers & Education, Vol. 45, No. 1. (August 2005), pp. 141-160.

The purpose of this paper is to show how data mining may offer promise as a strategy for discovering and building alternative representations for the data underlying asynchronous discussion forums. Presently, the instructor's view of the output of a threaded forum is limited to reviewing a transcript or print version of the written dialogue produced by participants. With potentially hundreds of contributions to review for an entire online course, the instructor lacks a comprehensive view of the information embedded in the transcript. In this context, the authors attempt to sort out the question, "what is data from an online forum?" among other key questions. The present work seeks to intersect the information (i.e., participation indicators) an instructor may wish to extract from the forum with viewable and useful information that the system could produce from the instructor's query. Temporal participation indicators are used to show how using data and text mining techniques in the query process could improve the instructor's ability to evaluate the progress of a threaded discussion.

A novel approach to fuzzy clustering based on a dissimilarity relation extracted from data using a TS system

Mario Cimino — 2008-01-24T14:13:18-00:00

Pattern Recognition, Vol. 39, No. 11. (November 2006), pp. 2077-2091.

Clustering refers to the process of unsupervised partitioning of a data set based on a dissimilarity measure, which determines the cluster shape. Considering that cluster shapes may change from one cluster to another, it would be of the utmost importance to extract the dissimilarity measure directly from the data by means of a data model. On the other hand, a model construction requires some kind of supervision of the data structure, which is exactly what we look for during clustering. So, the lower the supervision degree used to build the data model, the more it makes sense to resort to a data model for clustering purposes. Conscious of this, we propose to exploit very few pairs of patterns with known dissimilarity to build a TS system which models the dissimilarity relation. Among other things, the rules of the TS system provide an intuitive description of the dissimilarity relation itself. Then we use the TS system to build a dissimilarity matrix which is fed as input to an unsupervised fuzzy relational clustering algorithm, denoted any relation clustering algorithm (ARCA), which partitions the data set based on the proximity of the vectors containing the dissimilarity values between each pattern and all the other patterns in the data set. We show that combining the TS system and the ARCA algorithm allows us to achieve high classification performance on a synthetic data set and on two real data sets. Further, we discuss how the rules of the TS system represent a sort of linguistic description of the dissimilarity relation.

Web Mining: Clustering Web Documents A Preliminary Review

Khaled Hammouda — 2008-01-22T19:58:55-00:00

Evidently there is a tremendous proliferation in the amount of information found today on the largest shared information source, the World Wide Web (or simply the Web). The process of finding relevant information on the web can be overwhelming. Even with the presence of today’s search engines that index the web it is hard to wade through the large number of returned documents in a response to a user query. This fact has lead to the need to organize a large set of documents (due to a user query or simply a collection of documents) into categories through clustering. It is believed that grouping similar documents together into clusters will help the users find relevant information quicker, and will allow them to focus their search in the appropriate direction. The purpose of this review is an attempt to explore the clustering techniques in the data mining literature and to report on their appropriateness for clustering large sets of web documents. The review is by no means complete but covers the most representative approaches for clustering.

On Mining Web Access Logs

Anupam Joshi — 2008-01-22T08:14:59-00:00

(2000), pp. 63-69.

The proliferation of information on the world wide web has made the personalization of this information space a necessity. One possible approach to web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classification or clustering methods seem to be ideally suited to analyze the semi-structured log data of user accesses. In this paper, we define the notion of a "user...

Efficient algorithms for agglomerative hierarchical clustering methods

William Day — 2008-01-21T00:21:43-00:00

Journal of Classification, Vol. 1, No. 1. (1 December 1984), pp. 7-24.

Whenevern objects are characterized by a matrix of pairwise dissimilarities, they may be clustered by any of a number of sequential, agglomerative, hierarchical, nonoverlapping (SAHN) clustering methods. These SAHN clustering methods are defined by a paradigmatic algorithm that usually requires 0(n3) time, in the worst case, to cluster the objects. An improved algorithm (Anderberg 1973), while still requiring 0(n3) worst-case time, can reasonably be expected to exhibit 0(n2) expected behavior. By contrast, we describe a SAHN clustering algorithm that requires 0(n2 logn) time in the worst case. When SAHN clustering methods exhibit reasonable space distortion properties, further improvements are possible. We adapt a SAHN clustering algorithm, based on the efficient construction of nearest neighbor chains, to obtain a reasonably general SAHN clustering algorithm that requires in the worst case 0(n2) time and space.

Low-complexity fuzzy relational clustering algorithms for Web mining

R Krishnapuram — 2008-01-21T00:10:05-00:00

IEEE-FS, Vol. 9 (Aug. 2001), pp. 595-607.

Web site. The abstracts correspond to 10 topics (distance education, lament, health care, intermetallic, laminate, nuclear, aeronautics, plastic, trade, furnace, and recycling). There are about 100 abstracts per topic, but since the abstracts were not carefully chosen, some are outliers. In addition, we deliberately added 20 outliers. The second data set is a collection of 59 HTML documents compiled by 6 students at the Colorado School of Mines. Each student was asked to collect about 10 Web...

Social bookmarking and exploratory search

David Millen — 2007-10-30T17:03:06-00:00

ECSCW 2007 (2007), pp. 21-40.

In this paper, we explore various search tasks that are supported by a social bookmarking service. These bookmarking services hold great potential to powerfully combine personal tagging of information sources with interactive browsing, resulting in better social navigation. While there has been considerable interest in social tagging systems in recent years, little is known about their actual usage. In this paper, we present the results of a field study of a social bookmarking service that has been deployed in a large enterprise. We present new qualitative and quantitative data on how a corporate social tagging system was used, through both event logs (click level analysis) and interviews. We observed three types of search activities: community browsing, personal search, and explicit search. Community browsing was the most frequently used, and confirms the value of the social aspects of the system. We conclude that social bookmarking services support various kinds of exploratory search, and provide better personal bookmark management and enhance social navigation.

Improving web search ranking by incorporating user behavior information

Eugene Agichtein — 2007-05-08T22:04:16-00:00

(2006), pp. 19-26.

Incorporating user control into recommender systems based on naive bayesian classification

Verus Pronk — 2007-12-07T20:03:31-00:00

(2007), pp. 73-80.

IJCAI-05 Workshop on Intelligent Techniques for Web Personalization (ITWP05)

2008-01-02T09:13:58-00:00

(August 2005)

Social Adaptive Navigation Support for Open Corpus Electronic Textbooks

Peter Brusilovsky — 2007-12-31T02:12:57-00:00

Adaptive Hypermedia and Adaptive Web-Based Systems (2004), pp. 24-33.

Closed corpus AH systems demonstrate what is possible to achieve with adaptive hypermedia technologies; however they are impractical for dealing with the large volume of open corpus resources. Our system, Knowledge Sea II, presented in this paper explores social adaptive navigation support, an approach for providing personalized guidance in the open corpus context. Following the ideas of social navigation, we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The social adaptive navigation support implemented in our system was considered quite useful by students participating in the classroom study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigation support.

Talk amongst yourselves: inviting users to participate in online conversations

Maxwell Harper — 2007-02-03T17:47:50-00:00

(2007), pp. 62-71.

Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization

Bamshad Mobasher — 2007-11-28T00:32:28-00:00

Data Mining and Knowledge Discovery, Vol. 6, No. 1. (1 January 2002), pp. 61-82.

Web usage mining, possibly used in conjunction with standard approaches to personalization such as collaborative filtering, can help address some of the shortcomings of these techniques, including reliance on subjective user ratings, lack of scalability, and poor performance in the face of high-dimensional and sparse data. However, the discovery of patterns from usage data by itself is not sufficient for performing the personalization tasks. The critical step is the effective derivation of good quality and useful (i.e., actionable) “aggregate usage profiles” from these patterns. In this paper we present and experimentally evaluate two techniques, based on clustering of user transactions and clustering of pageviews, in order to discover overlapping aggregate profiles that can be effectively used by recommender systems for real-time Web personalization. We evaluate these techniques both in terms of the quality of the individual profiles generated, as well as in the context of providing recommendations as an integrated part of a personalization engine. In particular, our results indicate that using the generated aggregate profiles, we can achieve effective personalization at early stages of users' visits to a site, based only on anonymous clickstream data and without the benefit of explicit input by these users or deeper knowledge about them.

A framework for experimental evaluation of clustering techniques

R Koschke — 2007-03-06T17:25:55-00:00

Program Comprehension, 2000. Proceedings. IWPC 2000. 8th International Workshop on (2000), pp. 201-210.

Experimental evaluation of clustering techniques for component recovery is necessary in order to analyze their strengths and weaknesses in comparison to other techniques. For comparable evaluations of automatic clustering techniques, a common reference corpus of freely available systems is needed for which the actual components are known. The reference corpus is used to measure recall and precision of automatic techniques. For this measurement, a standard scheme for comparing the components recovered by a clustering technique to components in the reference corpus is required. This paper describes both the process of setting up reference corpora and ways of measuring recall and precision of automatic clustering techniques. For methods with human intervention, controlled experiments should be conducted. This paper additionally proposes a controlled experiment as a standard for evaluating manual and semi-automatic component recovery methods that can be conducted cost-effectively

Item-based top-n recommendation algorithms

M Deshpande — 2007-11-28T00:10:19-00:00

(2004)

this paper we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation...

Personalized recommendation driven by information flow

Xiaodan Song — 2006-12-12T04:40:41-00:00

(2006), pp. 509-516.

Collaborative Filtering: Fallacies and Insights in Measuring Similarity

Panagiotis — 2007-11-26T23:42:21-00:00

Nearest-neighbor collaborative filtering (CF) algorithms are gaining widespread acceptance in recommender systems and e-commerce applications. These algorithms provide recommendations for products, based on suggestions of users with similar preferences. One of the most crucial factors in the e#ectiveness of nearest-neighbor CF algorithms is the similarity measure that is used. The most popular measures are the Pearson correlation and cosine similarity. In this paper, we identify existing ...

Using Self-Similarity to Cluster Large Data Sets

Daniel Barbará — 2007-11-14T17:00:02-00:00

Data Mining and Knowledge Discovery, Vol. 7, No. 2. (1 April 2003), pp. 123-152.

Clustering is a widely used knowledge discovery technique. It helps uncovering structures in data that were not previously known. The clustering of large data sets has received a lot of attention in recent years, however, clustering is a still a challenging task since many published algorithms fail to do well in scaling with the size of the data set and the number of dimensions that describe the points, or in finding arbitrary shapes of clusters, or dealing effectively with the presence of noise. In this paper, we present a new clustering algorithm, based in self-similarity properties of the data sets. Self-similarity is the property of being invariant with respect to the scale used to look at the data set. While fractals are self-similar at every scale used to look at them, many data sets exhibit self-similarity over a range of scales. Self-similarity can be measured using the fractal dimension. The new algorithm which we call Fractal Clustering (FC) places points incrementally in the cluster for which the change in the fractal dimension after adding the point is the least. This is a very natural way of clustering points, since points in the same cluster have a great degree of self-similarity among them (and much less self-similarity with respect to points in other clusters). FC requires one scan of the data, is suspendable at will, providing the best answer possible at that point, and is incremental. We show via experiments that FC effectively deals with large data sets, high-dimensionality and noise and is capable of recognizing clusters of arbitrary shape.

Clustering the Users of Large Web Sites into Communities

Georgios Paliouras — 2007-11-06T00:34:51-00:00

(2000), pp. 719-726.

In this paper we analyze the performance of clustering methods on the task of constructing community models for the users of large Web sites.

Applying collaborative filtering techniques to movie search for better ranking and browsing

Seung-Taek Park — 2007-10-31T21:40:11-00:00

(2007), pp. 550-559.

Evaluating collaborative filtering recommender systems

Jonathan Herlocker — 2005-04-27T17:40:41-00:00

ACM Trans. Inf. Syst., Vol. 22, No. 1. (January 2004), pp. 5-53.

Computer Networks as Social Networks: Collaborative Work, Telework, and Virtual Community

B Wellman — 2007-10-17T04:45:27-00:00

Annual Review of Sociology, Vol. 22, No. 1. (1996), pp. 213-238.

Social network analysis: An approach and technique for the study of information exchange

C Haythornthwaite — 2007-10-17T04:50:54-00:00

Library and Information Science Research, Vol. 18, No. 4. (1996), pp. 323-342.

Let's browse: a collaborative Web browsing agent

Henry Lieberman — 2007-10-22T10:15:08-00:00

(1999), pp. 65-68.

Web browsing, like most of today's desktop applications, is usually a solitary activity. Other forms of media, such as watching television, are often done by groups of people, such as families or friends. What would it be like to do collaborative Web browsing? Could the computer provide assistance to group browsing by trying to help find mutual interests among the participants? Let's Browse is an experiment in building an agent to assist a group of people in browsing, by suggesting new...

An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback

Cyrus Shahabi — 2005-08-10T18:35:42-00:00

Distributed and Parallel Databases, Vol. 14, No. 2. (2003), pp. 173-192.

Knowledge discovery from users Web-page navigation

C Shahabi — 2007-10-22T10:13:11-00:00

Research Issues in Data Engineering, 1997. Proceedings. Seventh International Workshop on (1997), pp. 20-29.

The authors propose to detect users' navigation paths to the advantage of Web site owners. First, they explain the design and implementation of a profiler which captures a client's selected links and page order, accurate page viewing time and cache references, using a Java based remote agent. The information captured by the profiler is then utilized by a knowledge discovery technique to cluster users with similar interests. They introduce a novel path clustering method based on the similarity of the history of user navigation. This approach is capable of capturing the interests of the user which could persist through several subsequent hypertext link selections. Finally, they evaluate their path clustering technique via a simulation study on a sample WWW site. They show that, depending on the level of inserted noise, they can recover the correct clusters by 10%-27% of average error margin

Is Seeing Believing? How Recommender Interfaces Affect Users' Opinions

Dan Cosley — 2007-10-21T23:56:47-00:00

Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users....

REFEREE: An open framework for practical testing of recommender systems using ResearchIndex

Dan Cosley — 2007-10-21T23:55:04-00:00

(August February0--FebruaryMarch 2002)

Automated recommendation (e.g., personalized product recommendation on an ecommerce web site) is an increasingly valuable service associated with many databases---typically online retail catalogs and web logs. Currently, a major obstacle for evaluating recommendation algorithms is the lack of any standard, public, real-world testbed appropriate for the task. In an attempt to fill this gap, we have created REFEREE, a framework for building recommender systems using ResearchIndex---a...

RACOFI: A Rule-Applying Collaborative Filtering System

M Anderson — 2007-10-21T23:53:53-00:00

(2003)

In this paper we give an overview of the RACOFI (RuleApplying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms ST I...

Evaluation of Item-Based Top-N Recommendation Algorithms

George Karypis — 2006-09-06T19:18:08-00:00

(2001), pp. 247-254.

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows...

Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-based Approach

David Pennock — 2007-10-21T23:49:15-00:00

(2000), pp. 473-480.

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method...

Web Mining Research: A Survey

Kosala — 2005-05-06T15:37:01-00:00

SIGKDD: SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, ACM, Vol. 2 (2000)

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of...

Slope one predictors for online rating-based collaborative filtering

D Lemire — 2006-05-12T18:52:24-00:00

(2005)

Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f (x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates...

An algorithmic framework for performing collaborative filtering

Jonathan Herlocker — 2005-07-26T15:24:09-00:00

(1999), pp. 230-237.

Eigentaste: A Constant Time Collaborative Filtering Algorithm

Ken Goldberg — 2007-10-21T23:42:53-00:00

Information Retrieval, Vol. 4, No. 2. (2001), pp. 133-151.

Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of n users, standard nearest-neighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste...

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

John Breese — 2005-06-30T15:48:40-00:00

pp. 43-52.

Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation...

Identity and Deception in the Virtual Community

Judith Donath — 2005-03-14T21:52:13-00:00

Hybrid Recommender Systems: Survey and Experiments

Robin Burke — 2005-08-25T18:23:42-00:00

User Modeling and User-Adapted Interaction, Vol. 12, No. 4. (November 2002), pp. 331-370.

Towards Discovering Organizational Structure from Email Corpus

Ding Yang — 2007-10-11T18:40:05-00:00

Email logs people's communication history which provides valuable information regarding the infrastructure of an organization. In this paper, a two-phase framework is introduced to attack the problem of leadership discovery in an organization based on email communication history among the employees. Two heuristic metrics are proposed for evaluating pair-wise leadership factors among a group of employees. We also address several issues in discovering the organization's structure through mining...