Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
Recommender systems have emerged as an important part of the solution to the information overload problem facing today's Web users. Combining ideas and techniques from information filtering, user modeling, artificial intelligence, user interface design and human-computer interaction, recommender systems provide users with proactive suggestions that are tailored to meet their particular information needs and preferences. Indeed recommender systems have enjoyed considerable commercial success and continue to play an increasingly important role in many online services, from Amazon and iTunes to Tivo and Last.fm. It is our great pleasure to welcome you to the First ACM Recommender Systems conference, which builds on a wonderful legacy of research workshops and the Recommenders06 Summer School in Bilbao on the Present and Future of Recommender Systems. This conference brings together leaders in the field, from both research and practice, to explore the latest innovations, challenges, and opportunities for recommender systems technologies. The call for papers attracted 35 long paper submissions and 23 short paper submissions from 15 countries. After a detailed scientific review process the program committee selected 16 full-length papers (a 46% acceptance rate) and 14 short-papers (a 61% acceptance rate) for publication in the proceedings and presentation at the conference. In addition, the conference program includes a keynote presentation by Google's Krishna Bharat, the creator of Google News; two panel sessions filled with industry leaders sharing experiences and raising challenges for the research community; and industry-track submissions intended to provoke discussion. The conference also includes a doctoral symposium to provide an opportunity for doctoral students to explore and develop their research interests in an interdisciplinary workshop, under the guidance of a panel of distinguished research faculty.