update bib

docs/lenskit.bib

@@ -57,7 +57,7 @@ @inproceedings{caoMakingSystemsForget2015
 
 @inproceedings{carvalhoFAiRFrameworkAnalyses2018,
   title = {{{FAiR}}: {{A Framework}} for {{Analyses}} and {{Evaluations}} on {{Recommender Systems}}},
-  booktitle = {Computational {{Science}} and {{Its Applications}} \textendash{} {{ICCSA}} 2018},
+  booktitle = {Computational {{Science}} and {{Its Applications}} {\textendash} {{ICCSA}} 2018},
   author = {Carvalho, Diego and Silva, N{\'i}collas and Silveira, Thiago and Mour{\~a}o, Fernando and Pereira, Adriano and Dias, Diego and Rocha, Leonardo},
   year = {2018},
   pages = {383--397},
@@ -80,7 +80,7 @@ @article{dacremaTroublingAnalysisReproducibility2021
   address = {{New York, NY, USA}},
   issn = {1094-9224},
   doi = {10.1145/3434185},
-  abstract = {The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. To obtain a better understanding of the actual progress, we have compared recent results in the area of neural recommendation approaches based on collaborative filtering against a consistent set of existing simple baselines. The worrying outcome of the analysis of these recent works\textemdash all were published at prestigious scientific conferences between 2015 and 2018\textemdash is that 11 of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristic or linear models. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.1},
+  abstract = {The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. To obtain a better understanding of the actual progress, we have compared recent results in the area of neural recommendation approaches based on collaborative filtering against a consistent set of existing simple baselines. The worrying outcome of the analysis of these recent works{\textemdash}all were published at prestigious scientific conferences between 2015 and 2018{\textemdash}is that 11 of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristic or linear models. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.1},
   keywords = {evaluation,LensKit References,{reproducibility, Recommender systems, deep learning}}
 }
 
@@ -190,7 +190,7 @@ @misc{ekstrandTestingRecommenders2016
   month = feb,
   journal = {A Practical Guide to Building Recommender Systems},
   urldate = {2017-01-06},
-  abstract = {Why Test? When I met fellow GroupLens alum Sean McNee, he had a bit of advice for me: Write tests for your code. It took me some time to grasp the wisdom of this \textemdash{} after all, isn't it just re\ldots}
+  abstract = {Why Test? When I met fellow GroupLens alum Sean McNee, he had a bit of advice for me: Write tests for your code. It took me some time to grasp the wisdom of this {\textemdash} after all, isn't it just re{\ldots}}
 }
 
 @inproceedings{ekstrandWhenRecommendersFail2012,
@@ -232,7 +232,7 @@ @misc{funkNetflixUpdateTry2006
   year = {2006},
   month = dec,
   urldate = {2010-04-08},
-  howpublished = {http://sifter.org/\textasciitilde simon/journal/20061211.html},
+  howpublished = {http://sifter.org/{\textasciitilde}simon/journal/20061211.html},
   keywords = {Zotero Import (Mar 30),Zotero Import (Mar 30)/My Library,Zotero Import (Mar 30)/My Library/Eval Grant,Zotero Import (Mar 30)/My Library/LensKit,Zotero Import (Mar 30)/My Library/Recommender Systems,Zotero Import (Mar 30)/My Library/Recommender Systems/Error Analysis Paper,Zotero Import (Mar 30)/My Library/Recommender Systems/List Comparison Paper,Zotero Import (Mar 30)/My Library/Thesis}
 }
 
@@ -418,7 +418,7 @@ @inproceedings{lamNumbaLLVMbasedPython2015
 
 @misc{martinabadiTensorFlowLargeScaleMachine2015,
   title = {{{TensorFlow}}: {{Large-Scale Machine Learning}} on {{Heterogeneous Systems}}},
-  author = {{Mart\'in Abadi} and {Ashish Agarwal} and {Paul Barham} and {Eugene Brevdo} and {Zhifeng Chen} and {Craig Citro} and {Greg S. Corrado} and {Andy Davis} and {Jeffrey Dean} and {Matthieu Devin} and {Sanjay Ghemawat} and {Ian Goodfellow} and {Andrew Harp} and {Geoffrey Irving} and {Michael Isard} and Jia, Yangqing and {Rafal Jozefowicz} and {Lukasz Kaiser} and {Manjunath Kudlur} and {Josh Levenberg} and {Dandelion Man\'e} and {Rajat Monga} and {Sherry Moore} and {Derek Murray} and {Chris Olah} and {Mike Schuster} and {Jonathon Shlens} and {Benoit Steiner} and {Ilya Sutskever} and {Kunal Talwar} and {Paul Tucker} and {Vincent Vanhoucke} and {Vijay Vasudevan} and {Fernanda Vi\'egas} and {Oriol Vinyals} and {Pete Warden} and {Martin Wattenberg} and {Martin Wicke} and {Yuan Yu} and {Xiaoqiang Zheng}},
+  author = {{Mart{\'i}n Abadi} and {Ashish Agarwal} and {Paul Barham} and {Eugene Brevdo} and {Zhifeng Chen} and {Craig Citro} and {Greg S. Corrado} and {Andy Davis} and {Jeffrey Dean} and {Matthieu Devin} and {Sanjay Ghemawat} and {Ian Goodfellow} and {Andrew Harp} and {Geoffrey Irving} and {Michael Isard} and Jia, Yangqing and {Rafal Jozefowicz} and {Lukasz Kaiser} and {Manjunath Kudlur} and {Josh Levenberg} and {Dandelion Man{\'e}} and {Rajat Monga} and {Sherry Moore} and {Derek Murray} and {Chris Olah} and {Mike Schuster} and {Jonathon Shlens} and {Benoit Steiner} and {Ilya Sutskever} and {Kunal Talwar} and {Paul Tucker} and {Vincent Vanhoucke} and {Vijay Vasudevan} and {Fernanda Vi{\'e}gas} and {Oriol Vinyals} and {Pete Warden} and {Martin Wattenberg} and {Martin Wicke} and {Yuan Yu} and {Xiaoqiang Zheng}},
   year = {2015},
   keywords = {LensKit References}
 }
@@ -576,7 +576,7 @@ @article{rbp
   publisher = {{ACM}},
   issn = {1094-9224},
   doi = {10.1145/1416950.1416952},
-  abstract = {A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings \ldots}
+  abstract = {A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings {\ldots}}
 }
 
 @inproceedings{rendleBPRBayesianPersonalized2009,