@inproceedings{sarwar2001itembased, acmid = {372071}, address = {New York, NY, USA}, author = {Sarwar, Badrul and Karypis, George and Konstan, Joseph and Riedl, John}, booktitle = {Proceedings of the 10th international conference on World Wide Web}, doi = {10.1145/371920.372071}, interhash = {043d1aaba0f0b8c01d84edd517abedaf}, intrahash = {16f38785d7829500ed41c610a5eff9a2}, isbn = {1-58113-348-0}, location = {Hong Kong, Hong Kong}, numpages = {11}, pages = {285--295}, publisher = {ACM}, title = {Item-based collaborative filtering recommendation algorithms}, url = {http://doi.acm.org/10.1145/371920.372071}, year = 2001 } @incollection{wanner2011building, abstract = {Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.}, address = {Berlin/Heidelberg}, author = {Wanner, Leo and Vrochidis, Stefanos and Tonelli, Sara and Moßgraber, Jürgen and Bosch, Harald and Karppinen, Ari and Myllynen, Maria and Rospocher, Marco and Bouayad-Agha, Nadjet and Bügel, Ulrich and Casamayor, Gerard and Ertl, Thomas and Kompatsiaris, Ioannis and Koskentalo, Tarja and Mille, Simon and Moumtzidou, Anastasia and Pianta, Emanuele and Saggion, Horacio and Serafini, Luciano and Tarvainen, Virpi}, booktitle = {Environmental Software Systems. Frameworks of eEnvironment}, doi = {10.1007/978-3-642-22285-6_19}, editor = {Hřebíček, Jiří and Schimak, Gerald and Denzer, Ralf}, interhash = {2eb9871339618bc4be4afdfbbd3cda54}, intrahash = {ad68ea956a3bf495d64194ffce367a20}, isbn = {978-3-642-22284-9}, pages = {169--176}, publisher = {Springer}, series = {IFIP Advances in Information and Communication Technology}, title = {Building an Environmental Information System for Personalized Content Delivery}, url = {http://dx.doi.org/10.1007/978-3-642-22285-6_19}, volume = 359, year = 2011 } @book{pazosarias2012recommender, address = {Berlin/Heidelberg}, doi = {10.1007/978-3-642-25694-3}, editor = {and Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {56612672a3db5d6ea838e9bfa7b410cf}, intrahash = {aceda1417241eb872dc27db7c7e4158a}, isbn = {978-3-642-25693-6}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Recommender Systems for the Social Web}, url = {http://link.springer.com/book/10.1007/978-3-642-25694-3/page/1}, volume = 32, year = 2012 } @book{ricci2011recommender, abstract = {Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read. Recommender systems have proven to be valuable means for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed and during the last decade, many of them have also been successfully deployed in commercial environments. Development of recommender systems is a multi-disciplinary effort which involves experts from various fields such as Artificial intelligence, Human Computer Interaction, Information Technology, Data Mining, Statistics, Adaptive User Interfaces, Decision Support Systems, Marketing, or Consumer Behavior. Recommender Systems Handbook: A Complete Guide for Research Scientists and Practitioners aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. This is the first comprehensive book which is dedicated entirely to the field of recommender systems and covers several aspects of the major techniques. Its informative, factual pages will provide researchers, students and practitioners in industry with a comprehensive, yet concise and convenient reference source to recommender systems. The book describes in detail the classical methods, as well as extensions and novel approaches that were recently introduced. The book consists of five parts: techniques, applications and evaluation of recommender systems, interacting with recommender systems, recommender systems and communities, and advanced algorithms. The first part presents the most popular and fundamental techniques used nowadays for building recommender systems, such as collaborative filtering, content-based filtering, data mining methods and context-aware methods. The second part starts by surveying techniques and approaches that have been used to evaluate the quality of the recommendations. Then deals with the practical aspects of designing recommender systems, it describes design and implementation consideration, setting guidelines for the selection of the more suitable algorithms. The section continues considering aspects that may affect the design and finally, it discusses methods, challenges and measures to be applied for the evaluation of the developed systems. The third part includes papers dealing with a number of issues related to the presentation, browsing, explanation and visualization of the recommendations, and techniques that make the recommendation process more structured and conversational. The fourth part is fully dedicated to a rather new topic, which is however rooted in the core idea of a collaborative recommender, i.e., exploiting user generated content of various types to build new types and more credible recommendations. Finally the last section collects a few papers on some advanced topics, such as the exploitation of active learning principles to guide the acquisition of new knowledge, techniques suitable for making a recommender system robust against attacks of malicious users, and recommender systems that aggregate multiple types of user feedbacks and preferences to build more reliable recommendations. We would like to thank all authors for their valuable contributions. We would like to express gratitude for all reviewers that generously gave comments on drafts or counsel otherwise.We would like to express our special thanks to Susan Lagerstrom-Fife and staff members of Springer for their kind cooperation throughout the production of this book. Finally, we wish this handbook will contribute to the growth of this subject, we wish to the novices a fruitful learning path, and to those more experts a compelling application of the ideas discussed in this handbook and a fruitful development of this challenging research area. }, doi = {10.1007/978-0-387-85820-3}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, interhash = {fc9c45535069f00807f784abdd939d9f}, intrahash = {78d23da5e3ac4f79ba59f94ecf434cf6}, isbn = {978-0-387-85819-7}, publisher = {Springer US}, title = {Recommender Systems Handbook}, url = {http://link.springer.com/book/10.1007/978-0-387-85820-3/page/1}, year = 2011 } @inproceedings{mahmood2009improving, abstract = {Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.}, acmid = {1557930}, address = {New York, NY, USA}, author = {Mahmood, Tariq and Ricci, Francesco}, booktitle = {Proceedings of the 20th ACM conference on Hypertext and hypermedia}, doi = {10.1145/1557914.1557930}, interhash = {8b66c6c4995ed720d1b6b0029cbb36c9}, intrahash = {09f59f9da4949dd68dc0c7c3f8fb3e5b}, isbn = {978-1-60558-486-7}, location = {Torino, Italy}, numpages = {10}, pages = {73--82}, publisher = {ACM}, title = {Improving recommender systems with adaptive conversational strategies}, url = {http://doi.acm.org/10.1145/1557914.1557930}, year = 2009 } @article{chen2009interaction, abstract = {A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user’s preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user’s interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: }, author = {Chen, Li and Pu, Pearl}, doi = {10.1007/s11257-008-9057-x}, interhash = {a9feffd15221c15c499b2ac98ce7d03a}, intrahash = {f0e063a97473519ca650fe029da73ce7}, issn = {0924-1868}, journal = {User Modeling and User-Adapted Interaction}, language = {English}, number = 3, pages = {167--206}, publisher = {Springer Netherlands}, title = {Interaction design guidelines on critiquing-based recommender systems}, url = {http://dx.doi.org/10.1007/s11257-008-9057-x}, volume = 19, year = 2009 } @article{thompson2004personalized, abstract = {Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.}, acmid = {1622479}, author = {Thompson, Cynthia A. and Göker, Mehmet H. and Langley, Pat}, interhash = {76fe779bd06b36b82b6cb4456c4a2af1}, intrahash = {ea5a393bf4ccba3dd4e07b348199c202}, issn = {1076-9757}, issue_date = {January 2004}, journal = {Journal of Artificial Intelligence Research}, month = mar, number = 1, numpages = {36}, pages = {393--428}, publisher = {AI Access Foundation}, title = {A personalized system for conversational recommendations}, url = {http://dl.acm.org/citation.cfm?id=1622467.1622479}, volume = 21, year = 2004 } @article{montaner2003taxonomy, abstract = {Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have analyzed 37 different systems and their references andhave sorted them into a list of 8 basic dimensions. These dimensions arethen used to establish a taxonomy under which the systems analyzed areclassified. Finally, we conclude this paper with a cross-dimensionalanalysis with the aim of providing a starting point for researchers toconstruct their own recommender system.}, author = {Montaner, Miquel and López, Beatriz and de la Rosa, Josep Lluís}, doi = {10.1023/A:1022850703159}, interhash = {3753781e80f4118f1dd77d7637be2f8b}, intrahash = {f713e3f6acc112d9fbfd10216589d7db}, issn = {0269-2821}, journal = {Artificial Intelligence Review}, language = {English}, number = 4, pages = {285--330}, publisher = {Kluwer Academic Publishers}, title = {A Taxonomy of Recommender Agents on the Internet}, url = {http://dx.doi.org/10.1023/A%3A1022850703159}, volume = 19, year = 2003 } @incollection{rubens2011active, author = {Rubens, Neil and Kaplan, Dain and Sugiyama, Masashi}, booktitle = {Recommender Systems Handbook}, chapter = 23, doi = {10.1007/978-0-387-85820-3_23}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, interhash = {eab8d17924be10a7999ea09e6ed3be59}, intrahash = {e0b5682c1c228037aee63a459e2e2c62}, isbn = {978-0-387-85819-7}, language = {English}, pages = {735--767}, publisher = {Springer US}, title = {Active Learning in Recommender Systems}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_23}, year = 2011 } @article{koren2009matrix, abstract = {As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.}, author = {Koren, Y. and Bell, R. and Volinsky, C.}, doi = {10.1109/MC.2009.263}, interhash = {cface72aeba6ee8c561ccd15035d0ead}, intrahash = {59ab9b2678949949c04b0fe2a431585a}, issn = {0018-9162}, journal = {Computer}, month = aug, number = 8, pages = {30--37}, title = {Matrix Factorization Techniques for Recommender Systems}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5197422&tag=1}, volume = 42, year = 2009 }