The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
From x-rays to silly putty via Uranus: serendipity and its role in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CARES: a ranking-oriented CADAL recommender system
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Discovery is never by chance: designing for (un)serendipity
Proceedings of the seventh ACM conference on Creativity and cognition
Scholarly paper recommendation via user's recent research interests
Proceedings of the 10th annual joint conference on Digital libraries
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Serendipitous recommendations via innovators
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Classical music for rock fans?: novel recommendations for expanding user interests
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Serendipity occurs when one finds an interesting discovery while searching for something else. While search engines seek to report work relevant to a targeted query, recommendation engines are particularly well-suited for serendipitous recommendations as such processes do not need to fulfill a targeted query. Junior researchers can use such an engine to broaden their horizon and learn new areas, while senior researchers can discover interdisciplinary frontiers to apply integrative research. We adapt a state-of-the-art scholarly paper recommendation system's user profile construction to make use of information drawn from 1) dissimilar users and 2) co-authors to specifically target serendipitous recommendation.