add literature

01_Topic/topic.bib

@@ -224,4 +224,66 @@
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GCVWBMVC\\Peffers et al. - 2007 - A Design Science Research Methodology for Informat.pdf}
 }
 
+@inproceedings{atasSociallyAwareDiagnosisConstraintBased2019,
+  langid = {english},
+  location = {{Larnaca, Cyprus}},
+  title = {Socially-{{Aware Diagnosis}} for {{Constraint}}-{{Based Recommendation}}},
+  doi = {10.1145/3320435.3320436},
+  abstract = {Constraint-based group recommender systems support the identification of items that best match the individual preferences of all group members. In cases where the requirements of the group members are inconsistent with the underlying constraint set, the group needs to be supported such that an appropriate solution can be found. In this paper, we present a guided approach that determines socially-aware diagnoses based on different aggregation functions. We analyzed the prediction quality of different aggregation functions by using data collected in a user study. The results indicate that those diagnoses guided by the Least Misery aggregation function achieve a higher prediction quality compared to the Average Voting, Most Pleasure, and Majority Voting. Moreover, another major outcome of our work reveals that diagnoses based on aggregation functions outperform basic approaches such as Breadth First Search and Direct Diagnosis.},
+  eventtitle = {The 27th {{ACM Conference}}},
+  booktitle = {Proceedings of the 27th {{ACM Conference}} on {{User Modeling}}, {{Adaptation}} and {{Personalization}}  - {{UMAP}} '19},
+  publisher = {{ACM Press}},
+  date = {2019},
+  pages = {121-129},
+  author = {Atas, Muesluem and Samer, Ralph and Felfernig, Alexander and Tran, Thi Ngoc Trang and Erdeniz, Seda Polat and Stettinger, Martin},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3CYBXHM5\\Atas et al. - 2019 - Socially-Aware Diagnosis for Constraint-Based Reco.pdf}
+}
+
+@article{tiihonenIntroductionPersonalizationMass2017,
+  langid = {english},
+  title = {An Introduction to Personalization and Mass Customization},
+  volume = {49},
+  doi = {10.1007/s10844-017-0465-4},
+  abstract = {Mass customization as a state-of-the-art production paradigm aims to produce individualized, highly variant products and services with nearly mass production costs. A major side-effect for companies providing complex products and services is that customers quite often get confused by the high variety and do not make a purchase. Personalization technologies can help to alleviate the challenges of mass customization. These technologies support customers in specifying products and services that fit their wishes and needs in a fashion where decision and interaction efforts with sales support systems are significantly reduced. We provide a short overview of related research and the articles that are part of this special issue on Personalization and Mass Customization.},
+  number = {1},
+  journaltitle = {Journal of Intelligent Information Systems},
+  date = {2017-08},
+  pages = {1-7},
+  author = {Tiihonen, Juha and Felfernig, Alexander},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7LGAYLZB\\Tiihonen und Felfernig - 2017 - An introduction to personalization and mass custom.pdf}
+}
+
+@article{elahiSurveyActiveLearning2016,
+  langid = {english},
+  title = {A Survey of Active Learning in Collaborative Filtering Recommender Systems},
+  volume = {20},
+  issn = {15740137},
+  url = {https://linkinghub.elsevier.com/retrieve/pii/S1574013715300150},
+  doi = {10.1016/j.cosrev.2016.05.002},
+  abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.},
+  journaltitle = {Computer Science Review},
+  shortjournal = {Computer Science Review},
+  urldate = {2019-10-18},
+  date = {2016-05},
+  pages = {29-50},
+  author = {Elahi, Mehdi and Ricci, Francesco and Rubens, Neil},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4IR8QEFN\\Elahi et al. - 2016 - A survey of active learning in collaborative filte.pdf},
+  note = {ZSCC: 0000118}
+}
+
+@collection{ricciRecommenderSystemsHandbook2015,
+  langid = {english},
+  location = {{New York Heidelberg Dordrecht London}},
+  title = {Recommender Systems Handbook},
+  edition = {Second edition},
+  isbn = {978-1-4899-7636-9 978-1-4899-7637-6},
+  pagetotal = {1003},
+  publisher = {{Springer}},
+  date = {2015},
+  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\26HADE8N\\Ricci et al. - 2015 - Recommender systems handbook.pdf},
+  note = {ZSCC: NoCitationData[s0] 
+OCLC: 935904837}
+}
+
 

02_Proposal/proposal.bib

@@ -224,4 +224,66 @@
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\GCVWBMVC\\Peffers et al. - 2007 - A Design Science Research Methodology for Informat.pdf}
 }
 
+@inproceedings{atasSociallyAwareDiagnosisConstraintBased2019,
+  langid = {english},
+  location = {{Larnaca, Cyprus}},
+  title = {Socially-{{Aware Diagnosis}} for {{Constraint}}-{{Based Recommendation}}},
+  doi = {10.1145/3320435.3320436},
+  abstract = {Constraint-based group recommender systems support the identification of items that best match the individual preferences of all group members. In cases where the requirements of the group members are inconsistent with the underlying constraint set, the group needs to be supported such that an appropriate solution can be found. In this paper, we present a guided approach that determines socially-aware diagnoses based on different aggregation functions. We analyzed the prediction quality of different aggregation functions by using data collected in a user study. The results indicate that those diagnoses guided by the Least Misery aggregation function achieve a higher prediction quality compared to the Average Voting, Most Pleasure, and Majority Voting. Moreover, another major outcome of our work reveals that diagnoses based on aggregation functions outperform basic approaches such as Breadth First Search and Direct Diagnosis.},
+  eventtitle = {The 27th {{ACM Conference}}},
+  booktitle = {Proceedings of the 27th {{ACM Conference}} on {{User Modeling}}, {{Adaptation}} and {{Personalization}}  - {{UMAP}} '19},
+  publisher = {{ACM Press}},
+  date = {2019},
+  pages = {121-129},
+  author = {Atas, Muesluem and Samer, Ralph and Felfernig, Alexander and Tran, Thi Ngoc Trang and Erdeniz, Seda Polat and Stettinger, Martin},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3CYBXHM5\\Atas et al. - 2019 - Socially-Aware Diagnosis for Constraint-Based Reco.pdf}
+}
+
+@article{tiihonenIntroductionPersonalizationMass2017,
+  langid = {english},
+  title = {An Introduction to Personalization and Mass Customization},
+  volume = {49},
+  doi = {10.1007/s10844-017-0465-4},
+  abstract = {Mass customization as a state-of-the-art production paradigm aims to produce individualized, highly variant products and services with nearly mass production costs. A major side-effect for companies providing complex products and services is that customers quite often get confused by the high variety and do not make a purchase. Personalization technologies can help to alleviate the challenges of mass customization. These technologies support customers in specifying products and services that fit their wishes and needs in a fashion where decision and interaction efforts with sales support systems are significantly reduced. We provide a short overview of related research and the articles that are part of this special issue on Personalization and Mass Customization.},
+  number = {1},
+  journaltitle = {Journal of Intelligent Information Systems},
+  date = {2017-08},
+  pages = {1-7},
+  author = {Tiihonen, Juha and Felfernig, Alexander},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7LGAYLZB\\Tiihonen und Felfernig - 2017 - An introduction to personalization and mass custom.pdf}
+}
+
+@article{elahiSurveyActiveLearning2016,
+  langid = {english},
+  title = {A Survey of Active Learning in Collaborative Filtering Recommender Systems},
+  volume = {20},
+  issn = {15740137},
+  url = {https://linkinghub.elsevier.com/retrieve/pii/S1574013715300150},
+  doi = {10.1016/j.cosrev.2016.05.002},
+  abstract = {In collaborative filtering recommender systems user’s preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system’s recommendation accuracy. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount and type of information about the user’s tastes. Hence, specific techniques, which are defined as “active learning strategies”, can be used to selectively choose the items to be presented to the user for rating. In fact, an active learning strategy identifies and adopts criteria for obtaining data that better reflects users’ preferences and enables to generate better recommendations.},
+  journaltitle = {Computer Science Review},
+  shortjournal = {Computer Science Review},
+  urldate = {2019-10-18},
+  date = {2016-05},
+  pages = {29-50},
+  author = {Elahi, Mehdi and Ricci, Francesco and Rubens, Neil},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\4IR8QEFN\\Elahi et al. - 2016 - A survey of active learning in collaborative filte.pdf},
+  note = {ZSCC: 0000118}
+}
+
+@collection{ricciRecommenderSystemsHandbook2015,
+  langid = {english},
+  location = {{New York Heidelberg Dordrecht London}},
+  title = {Recommender Systems Handbook},
+  edition = {Second edition},
+  isbn = {978-1-4899-7636-9 978-1-4899-7637-6},
+  pagetotal = {1003},
+  publisher = {{Springer}},
+  date = {2015},
+  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\26HADE8N\\Ricci et al. - 2015 - Recommender systems handbook.pdf},
+  note = {ZSCC: NoCitationData[s0] 
+OCLC: 935904837}
+}
+