33,056 research outputs found
Using Search Engine Technology to Improve Library Catalogs
This chapter outlines how search engine technology can be used in online public access library
catalogs (OPACs) to help improve usersâ experiences, to identify usersâ intentions, and to indicate
how it can be applied in the library context, along with how sophisticated ranking criteria can be
applied to the online library catalog. A review of the literature and current OPAC developments
form the basis of recommendations on how to improve OPACs. Findings were that the major
shortcomings of current OPACs are that they are not sufficiently user-centered and that their results
presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0
developments. It is argued that OPAC development should be made search-centered before
additional features are applied. While the recommendations on ranking functionality and the use of
user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find
recommendations for developing better OPACs in this chapter. In short, readers will find a
systematic view on how the search enginesâ strengths can be applied to improving librariesâ online
catalogs
A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm
As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table
Detecting and Refactoring Operational Smells within the Domain Name System
The Domain Name System (DNS) is one of the most important components of the
Internet infrastructure. DNS relies on a delegation-based architecture, where
resolution of names to their IP addresses requires resolving the names of the
servers responsible for those names. The recursive structures of the inter
dependencies that exist between name servers associated with each zone are
called dependency graphs. System administrators' operational decisions have far
reaching effects on the DNSs qualities. They need to be soundly made to create
a balance between the availability, security and resilience of the system. We
utilize dependency graphs to identify, detect and catalogue operational bad
smells. Our method deals with smells on a high-level of abstraction using a
consistent taxonomy and reusable vocabulary, defined by a DNS Operational
Model. The method will be used to build a diagnostic advisory tool that will
detect configuration changes that might decrease the robustness or security
posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
Diverse Weighted Bipartite b-Matching
Bipartite matching, where agents on one side of a market are matched to
agents or items on the other, is a classical problem in computer science and
economics, with widespread application in healthcare, education, advertising,
and general resource allocation. A practitioner's goal is typically to maximize
a matching market's economic efficiency, possibly subject to some fairness
requirements that promote equal access to resources. A natural balancing act
exists between fairness and efficiency in matching markets, and has been the
subject of much research.
In this paper, we study a complementary goal---balancing diversity and
efficiency---in a generalization of bipartite matching where agents on one side
of the market can be matched to sets of agents on the other. Adapting a
classical definition of the diversity of a set, we propose a quadratic
programming-based approach to solving a supermodular minimization problem that
balances diversity and total weight of the solution. We also provide a scalable
greedy algorithm with theoretical performance bounds. We then define the price
of diversity, a measure of the efficiency loss due to enforcing diversity, and
give a worst-case theoretical bound. Finally, we demonstrate the efficacy of
our methods on three real-world datasets, and show that the price of diversity
is not bad in practice
Recurrent Session Approach to Generative Association Rule based Recommendation
This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot. In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability
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