74 research outputs found
Shedding light on a living lab: the CLEF NEWSREEL open recommendation platform
In the CLEF NEWSREEL lab, participants are invited to evaluate news recommendation techniques in real-time by providing news recommendations to actual users that visit commercial news portals to satisfy their information needs. A central role within this lab is the communication between participants and the users. This is enabled by The Open Recommendation Platform (ORP), a web-based platform which distributes users' impressions of news articles to the participants and returns their recommendations to the readers. In this demo, we illustrate the platform and show how requests are handled to provide relevant news articles in real-time
Overview of CLEF NEWSREEL 2014: News Recommendations Evaluation Labs
This paper summarises objectives, organisation, and results of the first
news recommendation evaluation lab (NEWSREEL 2014). NEWSREEL targeted
the evaluation of news recommendation algorithms in the form of a campaignstyle
evaluation lab. Participants had the chance to apply two types of evaluation
schemes. On the one hand, participants could apply their algorithms onto a data
set. We refer to this setting as off-line evaluation. On the other hand, participants
could deploy their algorithms on a server to interactively receive recommendation
requests. We refer to this setting as on-line evaluation. This setting ought to reveal
the actual performance of recommendation methods. The competition strived to
illustrate differences between evaluation with historical data and actual users. The
on-line evaluation does reflect all requirements which active recommender systems
face in practise. These requirements include real-time responses and large-scale
data volumes. We present the competitionâs results and discuss commonalities
regarding participantsâ approaches
Recommender Systems and Learning Analytics in TEL
Drachsler, H. (2011, 23 June). Recommender Systems and
Learning Analytics in TEL. Guest lecture at MUP/PLE lecture series, KMI, Open University UK.Technology-enhanced learning aims to design, develop and test socio-technical innovations that will support and enhance learning practices and knowledge sharing of individuals and organizations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. With the increasing use of Learning Management Systems, Personal Learning Environments, and Data Mashups the TEL field, became a promising application area for information retrieval technologies and Recommender Systems to suggest most suitable learning content or peers to learners. The renewed interest in information retrieval technologies in TEL reveals itself through an increasing number of scientific events and publications combined under the research term Learning Analytics. Learning Analytics has the potential for new insights into learning processes by making so far invisible patterns in the educational data visible to researchers and develop new services for educational practice.This lecture attempts to provide an introduction to Recommender Systems for TEL, as well as to highlight their particularities compared to recommender systems for other application domains. Finally, it will outline the latest developments of Recommender Systems in the area of Learning Analytics. The recording of both lecture can be found here: http://stadium.open.ac.uk/podium/.dataTEL, NeLLL AlterEg
A Broad Learning Approach for Context-Aware Mobile Application Recommendation
With the rapid development of mobile apps, the availability of a large number
of mobile apps in application stores brings challenge to locate appropriate
apps for users. Providing accurate mobile app recommendation for users becomes
an imperative task. Conventional approaches mainly focus on learning users'
preferences and app features to predict the user-app ratings. However, most of
them did not consider the interactions among the context information of apps.
To address this issue, we propose a broad learning approach for
\textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor
\textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to
effectively integrate user's preference, app category information and
multi-view features to facilitate the performance of app rating prediction. The
multidimensional structure is employed to capture the hidden relationships
between multiple app categories with multi-view features. We develop an
efficient factorization method which applies Tucker decomposition to learn the
full-order interactions within multiple categories and features. Furthermore,
we employ a group norm regularization to learn the group-wise
feature importance of each view with respect to each app category. Experiments
on two real-world mobile app datasets demonstrate the effectiveness of the
proposed method
DRec:Multidomain Recommendation System for Social Community
The Recommendation System is the software engines and the approaches for consideration proposal to the user which might be most probably matched with the liking of users. Usually, Recommendations system recommends on various fields like what items to buy, which movies to watch even the job recommendations, depending upon the users profile. Instinctively if the domains of users are captured and filtered out accordingly to recommend them will be a very useful idea. In this paper we will be discussing about the research done by us and the limitation of the system.We design a system for recommending domains in social network, using an explicit / offline data. We have tested it on two popular dataset namely Epinions and Ciao. The ratings of items are studied and performance measures are calculated with three different ways 1) MAP (Mean Average Precision) 2)F-measure and 3) nDCG (Normalized Discounted Cumulative Gain) . As well we have compared the results with 5 comparisons methods.All the techniques and methods are explained in paper.
DOI: 10.17762/ijritcc2321-8169.15081
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