12,598 research outputs found
The Partial Evaluation Approach to Information Personalization
Information personalization refers to the automatic adjustment of information
content, structure, and presentation tailored to an individual user. By
reducing information overload and customizing information access,
personalization systems have emerged as an important segment of the Internet
economy. This paper presents a systematic modeling methodology - PIPE
(`Personalization is Partial Evaluation') - for personalization.
Personalization systems are designed and implemented in PIPE by modeling an
information-seeking interaction in a programmatic representation. The
representation supports the description of information-seeking activities as
partial information and their subsequent realization by partial evaluation, a
technique for specializing programs. We describe the modeling methodology at a
conceptual level and outline representational choices. We present two
application case studies that use PIPE for personalizing web sites and describe
how PIPE suggests a novel evaluation criterion for information system designs.
Finally, we mention several fundamental implications of adopting the PIPE model
for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio
On the Localization of the Personalized PageRank of Complex Networks
In this paper new results on personalized PageRank are shown. We consider
directed graphs that may contain dangling nodes. The main result presented
gives an analytical characterization of all the possible values of the
personalized PageRank for any node.We use this result to give a theoretical
justification of a recent model that uses the personalized PageRank to classify
users of Social Networks Sites. We introduce new concepts concerning
competitivity and leadership in complex networks. We also present some
theoretical techniques to locate leaders and competitors which are valid for
any personalization vector and by using only information related to the
adjacency matrix of the graph and the distribution of its dangling nodes
Personalization by Partial Evaluation.
The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented
Web Site Personalization based on Link Analysis and Navigational Patterns
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users’ needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and the past users’ navigational patterns. In the vast majority of related algorithms, however, only the usage data are used to produce recommendations, disregarding the structural properties of the web graph. Thus important – in terms of PageRank authority score – pages may be underrated. In this work we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to the web pages based on their importance in the web site’s navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational sub-graphs for online web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches
PageRank Optimization by Edge Selection
The importance of a node in a directed graph can be measured by its PageRank.
The PageRank of a node is used in a number of application contexts - including
ranking websites - and can be interpreted as the average portion of time spent
at the node by an infinite random walk. We consider the problem of maximizing
the PageRank of a node by selecting some of the edges from a set of edges that
are under our control. By applying results from Markov decision theory, we show
that an optimal solution to this problem can be found in polynomial time. Our
core solution results in a linear programming formulation, but we also provide
an alternative greedy algorithm, a variant of policy iteration, which runs in
polynomial time, as well. Finally, we show that, under the slight modification
for which we are given mutually exclusive pairs of edges, the problem of
PageRank optimization becomes NP-hard.Comment: 30 pages, 3 figure
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