Clustering and indexing of experience sequences for popularity-driven recommendations

Kasim Candan, Mehmet E. Dönderler, J. Ramamoorthy, Jong W. Kim

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

2 Citations (Scopus)

Abstract

As part of our iCare efforts, we are developing mechanisms that provide guidance to individuals who are blind in diverse contexts. A fundamental challenge in this context is to represent and index experiences that can be used to provide recommendations. In this paper, we address the challenge of indexing experiences in order to retrieve them based on their popularities. In particular, we model experiences as sequences of propositional statements from a particular domain (daily life, web browsing, etc.). We then show that knowledge about domain constraints (such as commutativity between possible statements) need to be used for clustering and indexing experiences for popularity-search. We also highlight that don't cares (propositional statements not relevant to the user's query) make the task of popularity indexing challenging. Thus, we develop a canonical-sequence based approach that significantly reduces the experience sequence retrieval time in the presence of commutations. We introduce rule-compression, which helps achieve further reductions in the retrieval cost. We propose a novel two-level index structure, EXPdex, to efficiently answer wildcard (don't care) queries. We compare the proposed approach analytically and experimentally to a don't care-unaware solution, which does not take into account wildcards in queries while constructing the popularity index. Experiments show that the proposed approach provides large savings in retrieval time when commutations between the elements of sequences are allowed.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd ACM Workshop on Continuous Archival and Retrievalof Personal Experiences, CARPE'06
Pages75-84
Number of pages10
DOIs
StatePublished - 2006
Event3rd ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, CARPE'06 - Santa Barbara, CA, United States
Duration: Oct 23 2006Oct 27 2006

Other

Other3rd ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, CARPE'06
CountryUnited States
CitySanta Barbara, CA
Period10/23/0610/27/06

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Keywords

  • Assistive technology for blind users
  • Generating navigational recommendations
  • Indexing experiences

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

Cite this

Candan, K., Dönderler, M. E., Ramamoorthy, J., & Kim, J. W. (2006). Clustering and indexing of experience sequences for popularity-driven recommendations. In Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrievalof Personal Experiences, CARPE'06 (pp. 75-84) https://doi.org/10.1145/1178657.1178671

Clustering and indexing of experience sequences for popularity-driven recommendations. / Candan, Kasim; Dönderler, Mehmet E.; Ramamoorthy, J.; Kim, Jong W.

Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrievalof Personal Experiences, CARPE'06. 2006. p. 75-84.

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

Candan, K, Dönderler, ME, Ramamoorthy, J & Kim, JW 2006, Clustering and indexing of experience sequences for popularity-driven recommendations. in Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrievalof Personal Experiences, CARPE'06. pp. 75-84, 3rd ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, CARPE'06, Santa Barbara, CA, United States, 10/23/06. https://doi.org/10.1145/1178657.1178671
Candan K, Dönderler ME, Ramamoorthy J, Kim JW. Clustering and indexing of experience sequences for popularity-driven recommendations. In Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrievalof Personal Experiences, CARPE'06. 2006. p. 75-84 https://doi.org/10.1145/1178657.1178671
Candan, Kasim ; Dönderler, Mehmet E. ; Ramamoorthy, J. ; Kim, Jong W. / Clustering and indexing of experience sequences for popularity-driven recommendations. Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrievalof Personal Experiences, CARPE'06. 2006. pp. 75-84
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