@article{kermarrec2013towards, abstract = {The Web has become a user-centric platform where users post, share, annotate, comment and forward content be it text, videos, pictures, URLs, etc. This social dimension creates tremendous new opportunities for information exchange over the Internet, as exemplified by the surprising and exponential growth of social networks and collaborative platforms. Yet, niche content is sometimes difficult to retrieve using traditional search engines because they target the mass rather than the individual. Likewise, relieving users from useless notification is tricky in a world where there is so much information and so little of interest for each and every one of us. We argue that ultra-specific content could be retrieved and disseminated should search and notification be personalized to fit this new setting. We also argue that users’ interests should be implicitly captured by the system rather than relying on explicit classifications simply because the world is by nature unstructured, dynamic and users do not want to be hampered in their actions by a tight and static framework. In this paper, we review some existing personalization approaches, most of which are centralized. We then advocate the need for fully decentralized systems because personalization raises two main issues. Firstly, personalization requires information to be stored and maintained at a user granularity which can significantly hurt the scalability of a centralized solution. Secondly, at a time when the ‘big brother is watching you’ attitude is prominent, users may be more and more reluctant to give away their personal data to the few large companies that can afford such personalization. We start by showing how to achieve personalization in decentralized systems and conclude with the research agenda ahead.}, author = {Kermarrec, Anne-Marie}, doi = {10.1098/rsta.2012.0380}, eprint = {http://rsta.royalsocietypublishing.org/content/371/1987/20120380.full.pdf+html}, interhash = {5e6b8ecc53b6cdff9d8f7787c9a8bae1}, intrahash = {0bf7516712653f96af608ecb9222f7a3}, journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, month = mar, number = 1987, title = {Towards a personalized Internet: a case for a full decentralization}, url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120380.abstract}, volume = 371, year = 2013 } @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 } @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 } @incollection{goker2000personalized, abstract = {In this paper, we describe the Adaptive Place Advisor, a user adaptive, conversational recommendation system designed to help users decide on a destination, specifically a restaurant. We view the selection of destinations as an interactive, conversational process, with the advisory system inquiring about desired item characteristics and the human responding. The user model, which contains preferences regarding items, attributes, values, value combinations, and diversification, is also acquired during the conversation. The system enhances the user’s requirements with the user model and retrieves suitable items from a case-base. If the number of items found by the system is unsuitable (too high, too low) the next attribute to be constrained or relaxed is selected based on the information gain associated with the attributes. We also describe the current status of the system and future work.}, address = {Berlin/Heidelberg}, author = {Göker, Mehmet H. and Thompson, Cynthia A.}, booktitle = {Advances in Case-Based Reasoning}, doi = {10.1007/3-540-44527-7_10}, editor = {Blanzieri, Enrico and Portinale, Luigi}, interhash = {6a7ada84337e23c751663bcf569c0dbd}, intrahash = {5f020ff49356f96d199ac029d1b7c81a}, isbn = {978-3-540-67933-2}, language = {English}, pages = {99--111}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Personalized Conversational Case-Based Recommendation}, url = {http://dx.doi.org/10.1007/3-540-44527-7_10}, volume = 1898, year = 2000 } @inproceedings{rendle2010pairwise, abstract = {Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.

In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.}, acmid = {1718498}, address = {New York, NY, USA}, author = {Rendle, Steffen and Schmidt-Thieme, Lars}, booktitle = {Proceedings of the third ACM international conference on Web search and data mining}, doi = {10.1145/1718487.1718498}, interhash = {ce8fbdf2afb954579cdb58104fb683a7}, intrahash = {10fe730b391b08031f3103f9cdbb6e1a}, isbn = {978-1-60558-889-6}, location = {New York, New York, USA}, numpages = {10}, pages = {81--90}, publisher = {ACM}, title = {Pairwise interaction tensor factorization for personalized tag recommendation}, url = {http://doi.acm.org/10.1145/1718487.1718498}, year = 2010 } @techreport{haveliwala2003analytical, abstract = {PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of "importance" to web pages. Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way. We analytically compare three recent approaches to personalizing PageRank and discuss the tradeoffs of each one.}, address = {Stanford}, author = {Haveliwala, Taher and Kamvar, Sepandar and Jeh, Glen}, institution = {Stanford InfoLab}, interhash = {6adad5ffe99f07fe8777fa7e95da4021}, intrahash = {c0a97c488805a3b4349339439376ac44}, month = jun, number = {2003-35}, title = {An Analytical Comparison of Approaches to Personalizing PageRank}, url = {http://ilpubs.stanford.edu:8090/596/}, year = 2003 } @inproceedings{clements2007personalization, abstract = {This article describes a framework that captures collaborative tagging systems, and derives from it an overview of user tasks that qualify for personalization in such a system. Major research areas have focused on some of these tasks, but we identify many more opportunities. We propose a collaborative model that combines collaborative filtering and information retrieval techniques in order to assists the user to achieve these tasks. Based only on the user's tags, this personalization model assumes that a user's tags identify this user's taste. Because many users do not only tag the content that matches their taste, we propose an evaluating experiment that shows if rating information can be used to adjust the users' taste profiles. This experiment is one of the steps to advance to a completely personalized model, integrating user preference, content annotations and people relations.}, author = {Clements, M.}, booktitle = {Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007}, interhash = {4e817e20bc7caf0a8e1111e882700383}, intrahash = {fe43da7e093f06c36010358724d03b7b}, location = {Glasgow, UK}, month = aug, title = {Personalization of Social Media}, year = 2007 } @inproceedings{li1999powerbookmarks, abstract = {We extend the notion of bookmark management by introducing the functionali- ties of hypermedia databases. PowerBookmarks is a Web information organization, sharing, and management tool, which parses metadata from bookmarked URLs and uses it to index and classify the URLs. PowerBookmarks supports advanced query, classification, and navigation functionalities on collections of bookmarks. Power- Bookmarks monitors and utilizes users' access patterns to provide many useful per- sonalized services, such as automated URL bookmarking, document refreshing, and bookmark expiration. It also allows users to specify their preference in bookmark management, such as ranking schemes and classification tree structures. Subscrip- tion services for new or updated documents of users' interests are also supported.}, author = {Li, W. and Vu, Q. and Chang, E. and Agrawal, D. and Hara, Y. and Takano, H.}, booktitle = {Proceedings of the Eighth International World-Wide Web Conference}, file = {li1999powerbookmarks.pdf:li1999powerbookmarks.pdf:PDF}, interhash = {dcbc696803a6757e3f2f5c6d2cdbaa33}, intrahash = {aed0df6e449c07cd13b238a518612926}, lastdatemodified = {2005-08-20}, lastname = {Li}, longnotes = {long version}, month = May, own = {own}, pdf = {li99b.pdf}, read = {notread}, title = {PowerBookmarks: A System for Personalizable Web Information Organization}, url = {li99b.ps}, year = 1999 } @article{citeulike:171426, author = {Adomavicius, G. and Tuzhilin, A.}, citeulike-article-id = {171426}, interhash = {42f7653127a823354d000ea95cf804be}, intrahash = {55294392edb717922798725dd8be80b3}, journal = {Knowledge and Data Engineering, IEEE Transactions on}, keywords = {collaborative collaborative-filtering filtering mining personalization recommender recommender-systems systems}, number = 6, pages = {734--749}, priority = {2}, title = {Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1423975}, volume = 17, year = 2005 } @proceedings{semwebpers2004, editor = {Mobasher, Bamshad and Anand, Sarabjot Singh and Berendt, Bettina and Hotho, Andreas}, interhash = {b0c15639284c2600789ff7f36a825aa6}, intrahash = {3d91da8819141c3bd4448a174b181db1}, month = SEP, note = {\url{http://maya.cs.depaul.edu/~mobasher/swp04/}}, title = {Semantic Web Personalization}, year = 2004 } @article{MobasheretalCACM, author = {Mobasher, B. and Cooley, R. and Srivastava, J.}, interhash = {98d5090dafb39596483c75dc4a6846c3}, intrahash = {a7a6cdb6e0790b276d7f0642991e734e}, journal = {Communications of the ACM}, location = {Santa Barbara, CA}, number = 8, pages = {142--151}, title = {Automatic personalization based on Web usage mining}, volume = 43, year = 2000 }