Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Improving Personalization Solutions through Optimal Segmentation of Customer Bases
IEEE Transactions on Knowledge and Data Engineering
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Alleviating Cold-Start Problem by Using Implicit Feedback
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Proceedings of the fifth ACM conference on Recommender systems
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
Opinion-based User Profile Modeling for Contextual Suggestions
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Hidden factors and hidden topics: understanding rating dimensions with review text
Proceedings of the 7th ACM conference on Recommender systems
Recommendation using textual opinions
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
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Online hotel searching is a daunting task due to the wealth of online information. Reviews written by other travelers replace the word-of-mouth, yet turn the search into a time consuming task. Users do not rate enough hotels to enable a collaborative filtering based recommendation. Thus, a cold start recommender system is needed. In this work we design a cold start hotel recommender system, which uses the text of the reviews as its main data. We define context groups based on reviews extracted from TripAdvisor.com and Venere.com. We introduce a novel weighted algorithm for text mining. Our algorithm imitates a user that favors reviews written with the same trip intent and from people of similar background (nationality) and with similar preferences for hotel aspects, which are our defined context groups. Our approach combines numerous elements, including unsupervised clustering to build a vocabulary for hotel aspects, semantic analysis to understand sentiment towards hotel features, and the profiling of intent and nationality groups. We implemented our system which was used by the public to conduct 150 trip planning experiments. We compare our solution to the top suggestions of the mentioned web services and show that users were, on average, 20% more satisfied with our hotel recommendations. We outperform these web services even more in cities where hotel prices are high.