GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Multilevel k-way hypergraph partitioning
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Proceedings of the 11th international conference on World Wide Web
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Community-Based Recommendation System to Reveal Unexpected Interests
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering frequent topological structures from graph datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Relevance search and anomaly detection in bipartite graphs
ACM SIGKDD Explorations Newsletter
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring and extracting proximity in networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Effective label acquisition for collective classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Electricity based external similarity of categorical attributes
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Parallelizing Random Walk with Restart for large-scale query recommendation
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
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
Serendipitous fuzzy item recommendation with profilematcher
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
"Tell me more": finding related items from user provided feedback
DS'11 Proceedings of the 14th international conference on Discovery science
Bisociative discovery of interesting relations between domains
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Improving the performance of recommender system by exploiting the categories of products
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
New approaches to diversity and novelty in recommender systems
FDIA'11 Proceedings of the Fourth BCS-IRSG conference on Future Directions in Information Access
Effective next-items recommendation via personalized sequential pattern mining
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Discovering unknown but interesting items on personal social network
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Towards discovery of subgraph bisociations
Bisociative Knowledge Discovery
A group recommendation approach for service selection
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
CredibleWeb: a platform for web credibility evaluation
CHI '13 Extended Abstracts on Human Factors in Computing Systems
TopRec: domain-specific recommendation through community topic mining in social network
Proceedings of the 22nd international conference on World Wide Web
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
Review: A review of novelty detection
Signal Processing
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Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored with his/her usual genre of items (e.g., books, movies, hobbies), and may want a recommendation that is related, but off the beaten path, possibly leading to a new genre of books/movies/hobbies. How would we define, as well as automate, this seemingly selfcontradicting request? We introduce TANGENT, a novel recommendation algorithm to solve this problem. The main idea behind TANGENT is to envision the problem as node selection on a graph, giving high scores to nodes that are well connected to the older choices, and at the same time well connected to unrelated choices. The method is carefully designed to be (a) parameter-free (b) effective and (c) fast. We illustrate the benefits of TANGENT with experiments on both synthetic and real data sets. We show that TANGENT makes reasonable, yet surprising, horizon-broadening recommendations. Moreover, it is fast and scalable, since it can easily use existing fast algorithms on graph node proximity.