The future success of these systems depends on more than a Netflix challenge. Recommender systems have become a ubiquitous part of our daily online user experience and support users in a variety of domains. Today, the scientific community operationalizes the research problem mainly on principles from information retrieval and machine learning, leading to a well-defined but narrow problem characterization. We briefly review the history of the field, report on the recent advances, and propose a more comprehensive research approach that considers both the consumer's and the provider's perspective.
%0 Journal Article
%1 JannachResnickEtAl16cacm
%A Jannach, Dietmar
%A Resnick, Paul
%A Tuzhilin, Alexander
%A Zanker, Markus
%D 2016
%J Communications of the ACM
%K 01801 acm paper ai adaptive user interface assist information retrieval zzz.iui
%N 11
%P 94--102
%R 10.1145/2891406
%T Recommender Systems---Beyond Matrix Completion
%V 59
%X The future success of these systems depends on more than a Netflix challenge. Recommender systems have become a ubiquitous part of our daily online user experience and support users in a variety of domains. Today, the scientific community operationalizes the research problem mainly on principles from information retrieval and machine learning, leading to a well-defined but narrow problem characterization. We briefly review the history of the field, report on the recent advances, and propose a more comprehensive research approach that considers both the consumer's and the provider's perspective.
@article{JannachResnickEtAl16cacm,
abstract = {The future success of these systems depends on more than a Netflix challenge. Recommender systems have become a ubiquitous part of our daily online user experience and support users in a variety of domains. Today, the scientific community operationalizes the research problem mainly on principles from information retrieval and machine learning, leading to a well-defined but narrow problem characterization. We briefly review the history of the field, report on the recent advances, and propose a more comprehensive research approach that considers both the consumer's and the provider's perspective.},
added-at = {2016-11-02T10:36:15.000+0100},
author = {Jannach, Dietmar and Resnick, Paul and Tuzhilin, Alexander and Zanker, Markus},
biburl = {https://www.bibsonomy.org/bibtex/2fbef9a13cdd104beb7ea75873c30d8d7/flint63},
doi = {10.1145/2891406},
file = {ACM Digital Library:2016/JannachResnickEtAl16cacm.pdf:PDF},
groups = {public},
interhash = {8cd379776b79042e33a27be1636ec475},
intrahash = {fbef9a13cdd104beb7ea75873c30d8d7},
issn = {0001-0782},
journal = {Communications of the ACM},
keywords = {01801 acm paper ai adaptive user interface assist information retrieval zzz.iui},
month = {#nov#},
number = 11,
pages = {94--102},
timestamp = {2018-04-16T11:50:24.000+0200},
title = {Recommender Systems---Beyond Matrix Completion},
username = {flint63},
volume = 59,
year = 2016
}