8,820 research outputs found
Investigating the information-seeking behaviour of academic lawyers: From Ellis's model to design.
Information-seeking is important for lawyers, who have access to many dedicated electronic resources.However there is considerable scope for improving the design of these resources to better support information-seeking. One way of informing design is to use information-seeking models as theoretical lenses to analyse usersā behaviour with existing systems. However many models, including those informed by studying lawyers, analyse information-seeking at a high level of abstraction and are only likely to lead to broad-scoped design insights. We illustrate that one potentially useful (and lowerlevel) model is Ellisās - by using it as a lens to analyse and make design suggestions based on the information-seeking behaviour of twenty-seven academic lawyers, who were asked to think aloud whilst using electronic legal resources to find information for their work. We identify similar information-seeking behaviours to those originally found by Ellis and his colleagues in scientific domains, along with several that were not identified in previous studies such as āupdatingā (which we believe is particularly pertinent to legal information-seeking). We also present a refinement of Ellisās model based on the identification of several levels that the behaviours were found to operate at and the identification of sets of mutually exclusive subtypes of behaviours
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
Contextualised Browsing in a Digital Library's Living Lab
Contextualisation has proven to be effective in tailoring \linebreak search
results towards the users' information need. While this is true for a basic
query search, the usage of contextual session information during exploratory
search especially on the level of browsing has so far been underexposed in
research. In this paper, we present two approaches that contextualise browsing
on the level of structured metadata in a Digital Library (DL), (1) one variant
bases on document similarity and (2) one variant utilises implicit session
information, such as queries and different document metadata encountered during
the session of a users. We evaluate our approaches in a living lab environment
using a DL in the social sciences and compare our contextualisation approaches
against a non-contextualised approach. For a period of more than three months
we analysed 47,444 unique retrieval sessions that contain search activities on
the level of browsing. Our results show that a contextualisation of browsing
significantly outperforms our baseline in terms of the position of the first
clicked item in the result set. The mean rank of the first clicked document
(measured as mean first relevant - MFR) was 4.52 using a non-contextualised
ranking compared to 3.04 when re-ranking the result lists based on similarity
to the previously viewed document. Furthermore, we observed that both
contextual approaches show a noticeably higher click-through rate. A
contextualisation based on document similarity leads to almost twice as many
document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201
Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
Personalized recommender systems rely on each user's personal usage data in
the system, in order to assist in decision making. However, privacy policies
protecting users' rights prevent these highly personal data from being publicly
available to a wider researcher audience. In this work, we propose a memory
biased random walk model on multilayer sequence network, as a generator of
synthetic sequential data for recommender systems. We demonstrate the
applicability of the synthetic data in training recommender system models for
cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape
Social Information Processing in Social News Aggregation
The rise of the social media sites, such as blogs, wikis, Digg and Flickr
among others, underscores the transformation of the Web to a participatory
medium in which users are collaboratively creating, evaluating and distributing
information. The innovations introduced by social media has lead to a new
paradigm for interacting with information, what we call 'social information
processing'. In this paper, we study how social news aggregator Digg exploits
social information processing to solve the problems of document recommendation
and rating. First, we show, by tracking stories over time, that social networks
play an important role in document recommendation. The second contribution of
this paper consists of two mathematical models. The first model describes how
collaborative rating and promotion of stories emerges from the independent
decisions made by many users. The second model describes how a user's
influence, the number of promoted stories and the user's social network,
changes in time. We find qualitative agreement between predictions of the model
and user data gathered from Digg.Comment: Extended version of the paper submitted to IEEE Internet Computing's
special issue on Social Searc
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
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