Tangent: A novel, "Surprise-me", recommendation algorithm

Kensuke Onuma, Hanghang Tong, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

81 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD '09
Subtitle of host publicationProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages657-665
Number of pages9
DOIs
StatePublished - Nov 9 2009
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
CountryFrance
CityParis
Period6/28/097/1/09

Keywords

  • Algorithms
  • Experimentation

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'Tangent: A novel, "Surprise-me", recommendation algorithm'. Together they form a unique fingerprint.

  • Cite this

    Onuma, K., Tong, H., & Faloutsos, C. (2009). Tangent: A novel, "Surprise-me", recommendation algorithm. In KDD '09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 657-665). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1557019.1557093