Alan Said
Alan Said
Home
Bio
Talks
Publications
Service
Press & Media
Light
Dark
Automatic
Paper-Conference
Challenges on Combining Open Web and Dataset Evaluation Results: The Case type: publication profile: false of the Contextual Suggestion Track
The TREC 2013 Contextual Suggestion Track allowed participants to submit personalised rankings using documents either from the OpenWeb …
Alejandro Bellogín
,
Thaer Samar
,
Arjen P. De Vries
,
Alan Said
Jan 1, 2014
Cite
DOI
Comparative evaluation of recommender systems for digital media
TV operators and content providers use recommender systems to connect consumers directly with content that fits their needs, their …
D. Tikk
,
R. Turrin
,
M. Larson
,
D. Zibriczky
,
D. Malagoli
,
Alan Said
,
A. Lommatzsch
,
V. Gál
,
S. Székely
Cite
DOI
URL
Comparative recommender system evaluation: Benchmarking recommendation frameworks
Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the …
Alan Said
,
A. Bellogín
Cite
DOI
Do recommendations matter? News recommendation in real life
We present a study of how recommendations are received in real life by users across different news domains (traditional online …
Alan Said
,
A. Bellogín
,
J. Lin
,
A. De Vries
Cite
DOI
Free lunch enhancement for collaborative filtering with factorization machines
The advantage of Factorization Machines over other factorization models is their ability to easily integrate and efficiently exploit …
B. Loni
,
Alan Said
,
M. Larson
,
A. Hanjalic
Cite
DOI
Recommender systems challenge 2014
The 2014 ACM Recommender Systems Challenge invited re- searchers and practitioners to work towards a common goal, this goal being the …
Alan Said
,
S. Dooms
,
B. Loni
,
D. Tikk
Cite
DOI
RiVal - A toolkit to foster reproducibility in recommender system evaluation
Currently, it is diffcult to put in context and compare the results from a given evaluation of a recommender system, mainly because too …
Alan Said
,
A. Bellogín
Cite
DOI
The Magic Barrier of Recommender Systems – No Magic, Just Ratings
Recommender Systems need to deal with different types of users who represent their preferences in various ways. This difference in user …
Alejandro Bellogín
,
Alan Said
,
Arjen P. De Vries
Cite
DOI
User-Item Reciprocity in Recommender Systems: Incentivizing the Crowd
Alan Said
,
Martha Larson
,
Domonkos Tikk
,
Paolo Cremonesi
,
Alexandros Karatzoglou
,
Frank Hopfgartner
,
Roberto Turrin
,
Joost Geurts
Cite
WrapRec: An easy extension of recommender system libraries
WrapRec is an easy-to-use Recommender Systems toolkit which allows users to easily implement or wrap recommen- dation algorithms from …
B. Loni
,
Alan Said
Cite
DOI
«
»
Cite
×