add literature

30_Thesis/thesis.bib

@@ -490,6 +490,23 @@
   langid = {english}
 }
 
+@incollection{felfernigGroupRecommenderApplications2018,
+  title = {Group {{Recommender Applications}}},
+  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
+  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
+  editor = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
+  date = {2018},
+  pages = {75--89},
+  publisher = {{Springer International Publishing}},
+  location = {{Cham}},
+  doi = {10.1007/978-3-319-75067-5_4},
+  abstract = {In this chapter, we present an overview of different group recommender applications. We organize this overview into the application domains of music, movies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chap. 2.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\49BRYWWD\\Felfernig et al_2018_Group Recommender Applications.pdf},
+  isbn = {978-3-319-75067-5},
+  langid = {english},
+  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}}
+}
+
 @book{felfernigGroupRecommenderSystems2018,
   title = {Group {{Recommender Systems An Introduction}}},
   shorttitle = {Group Recommender Systems},
@@ -612,6 +629,24 @@
   langid = {english}
 }
 
+@inproceedings{garciaGroupRecommenderSystem2009,
+  title = {A {{Group Recommender System}} for {{Tourist Activities}}},
+  booktitle = {E-{{Commerce}} and {{Web Technologies}}},
+  author = {Garcia, Inma and Sebastia, Laura and Onaindia, Eva and Guzman, Cesar},
+  editor = {Di Noia, Tommaso and Buccafurri, Francesco},
+  date = {2009},
+  pages = {26--37},
+  publisher = {{Springer}},
+  location = {{Berlin, Heidelberg}},
+  doi = {10.1007/978-3-642-03964-5_4},
+  abstract = {This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven search engine that manages the group recommendation.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\JDW2ZGP8\\Garcia et al_2009_A Group Recommender System for Tourist Activities.pdf},
+  isbn = {978-3-642-03964-5},
+  keywords = {Group Recommenders,Recommender Systems,Tourism},
+  langid = {english},
+  series = {Lecture {{Notes}} in {{Computer Science}}}
+}
+
 @article{glogerScrumPradigmenwechselIm2010,
   title = {Scrum: Der Pradigmenwechsel im Projekt- und Produktmanagement \textendash{} Eine Einf\"uhrung},
   shorttitle = {Scrum},
@@ -1050,6 +1085,24 @@ procedure.},
   number = {3}
 }
 
+@article{peraGroupRecommenderMovies2013,
+  title = {A Group Recommender for Movies Based on Content Similarity and Popularity},
+  author = {Pera, Maria S. and Ng, Yiu-Kai},
+  date = {2013-05-01},
+  journaltitle = {Information Processing \& Management},
+  shortjournal = {Information Processing \& Management},
+  volume = {49},
+  pages = {673--687},
+  issn = {0306-4573},
+  doi = {10.1016/j.ipm.2012.07.007},
+  abstract = {People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs of a group (rather than the interests of individual group members). With a number of new movies being released every week, online recommenders play a significant role in suggesting movies for family members or groups of friends/people to watch, either at home or at movie theaters. Making group recommendations relevant to the joint interests of a group, however, is not a trivial task due to the diversity in preferences among group members. To address this issue, we introduce GroupReM which makes movie recommendations appealing (to a certain degree) to members of a group by (i) employing a merging strategy to explore individual group members' interests in movies and create a profile that reflects the preferences of the group on movies, (ii) using word-correlation factors to find movies similar in content, and (iii) considering the popularity of movies at a movie website. Unlike existing group recommenders based on collaborative filtering (CF) which consider ratings of movies to perform the recommendation task, GroupReM primarily employs (personal) tags for capturing the contents of movies considered for recommendation and group members' interests. The design of GroupReM, which is simple and domain-independent, can easily be extended to make group recommendations on items other than movies. Empirical studies conducted using more than 3000 groups of different users in the MovieLens dataset, which are various in terms of numbers and preferences in movies, show that GroupReM is highly effective and efficient in recommending movies appealing to a group. Experimental results also verify that GroupReM outperforms popular CF-based recommenders in making group recommendations.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\KXUX8URG\\Pera_Ng_2013_A group recommender for movies based on content similarity and popularity.pdf;C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\BWWR8DFF\\S0306457312001045.html},
+  keywords = {Content-similarity,Group recommender,Movie,Popularity},
+  langid = {english},
+  number = {3},
+  series = {Personalization and {{Recommendation}} in {{Information Access}}}
+}
+
 @article{pereiraFeatureBasedPersonalizedRecommender2016,
   title = {A {{Feature}}-{{Based Personalized Recommender System}} for {{Product}}-{{Line Configuration}}},
   author = {Pereira, Juliana Alves and Matuszyk, Pawel and Krieter, Sebastian and Spiliopoulou, Myra and Saake, Gunter},
@@ -1106,6 +1159,14 @@ procedure.},
   langid = {english}
 }
 
+@inproceedings{piliponyte2013sequential,
+  title = {Sequential Music Recommendations for Groups by Balancing User Satisfaction.},
+  booktitle = {{{UMAP}} Workshops},
+  author = {Piliponyte, Auste and Ricci, Francesco and Koschwitz, Julian},
+  date = {2013},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\Y9EFUITE\\Piliponyte et al. - Sequential Music Recommendations for Groups by Bal.pdf}
+}
+
 @unpublished{pydataExamplePredictiveAnalytics16:42:00UTC,
   title = {An {{Example}} of {{Predictive Analytics}}: {{Building}} a {{Recommendation Engine}} \ldots{}},
   shorttitle = {An {{Example}} of {{Predictive Analytics}}},