add literature

01_Topic/topic.bib

@@ -465,4 +465,41 @@ OCLC: 935904837}
   note = {OCLC: 636781070}
 }
 
+@article{felfernigConstraintbasedRecommenderSystems,
+  langid = {english},
+  title = {Constraint-Based {{Recommender Systems}}: {{Technologies}} and {{Research Issues}}},
+  abstract = {Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.},
+  pages = {10},
+  author = {Felfernig, A and Burke, R},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\BBYQH8IW\\Felfernig und Burke - Constraint-based Recommender Systems Technologies.pdf}
+}
+
+@article{burkeHybridRecommenderSystems,
+  langid = {english},
+  title = {Hybrid {{Recommender Systems}}: {{Survey}} and {{Experiments}}},
+  abstract = {Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative ¢ltering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative ¢ltering.},
+  pages = {40},
+  author = {Burke, Robin},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\7DKPN9IX\\Burke - Hybrid Recommender Systems Survey and Experiments.pdf}
+}
+
+@article{hernandezdelolmoEvaluationRecommenderSystems2008,
+  langid = {english},
+  title = {Evaluation of Recommender Systems: {{A}} New Approach},
+  volume = {35},
+  issn = {09574174},
+  url = {https://linkinghub.elsevier.com/retrieve/pii/S0957417407002928},
+  doi = {10.1016/j.eswa.2007.07.047},
+  shorttitle = {Evaluation of Recommender Systems},
+  abstract = {It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but specific enough so that we can obtain solid results. In this paper, we propose such a framework, attempting to extract the essential features of recommender systems. In this framework, the most essential feature is the objective of the recommender system. What is more, in this paper, recommender systems are viewed as applications with the following essential objective. Recommender systems must: (i) choose which (of the items) should be shown to the user, (ii) decide when and how the recommendations must be shown. Next, we will show that a new metric emerges naturally from this framework. Finally, we will conclude by comparing the properties of this new metric with the traditional ones. Among other things, we will show that we may evaluate the whole range of recommender systems with this single metric.},
+  number = {3},
+  journaltitle = {Expert Systems with Applications},
+  shortjournal = {Expert Systems with Applications},
+  urldate = {2019-10-25},
+  date = {2008-10},
+  pages = {790-804},
+  author = {Hernández del Olmo, Félix and Gaudioso, Elena},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\B5DFYUDA\\Hernández del Olmo und Gaudioso - 2008 - Evaluation of recommender systems A new approach.pdf}
+}
+