Item Relevance Measure

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An Item Relevance Measure is a user-system performance measure for the relevance of an interactable item to some user population.



References

2017

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Relevance_(information_retrieval)#Clustering_and_relevance Retrieved:2017-6-22.
    • The cluster hypothesis, proposed by C. J. van Rijsbergen in 1979, asserts that two documents that are similar to each other have a high likelihood of being relevant to the same information need. With respect to the embedding similarity space, the cluster hypothesis can be interpreted globally or locally.F. Diaz, Autocorrelation and Regularization of Query-Based Retrieval Scores. PhD thesis, University of Massachusetts Amherst, Amherst, MA, February 2008, Chapter 3. The global interpretation assumes that there exist some fixed set of underlying topics derived from inter-document similarity. These global clusters or their representatives can then be used to relate relevance of two documents (e.g. two documents in the same cluster should both be relevant to the same request). Methods in this spirit include:
      • cluster-based information retrievalW. B. Croft, “A model of cluster searching based on classification,” Information Systems, vol. 5, pp. 189–195, 1980. A. Griffiths, H. C. Luckhurst, and P. Willett, “Using interdocument similarity information in document retrieval systems,” Journal of the American Society for Information Science, vol. 37, no. 1, pp. 3–11, 1986.
      • cluster-based document expansion such as latent semantic analysis or its language modeling equivalents.X. Liu and W. B. Croft, “Cluster-based retrieval using language models,” in SIGIR ’04: Proceedings of the 27th annual International Conference on Research and development in information retrieval, (New York, NY, USA), pp. 186–193, ACM Press, 2004. It is important to ensure that clusters – either in isolation or combination – successfully model the set of possible relevant documents.
    • A second interpretation, most notably advanced by Ellen Voorhees,E. M. Voorhees, “The cluster hypothesis revisited,” in SIGIR ’85: Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval, (New York, NY, USA), pp. 188–196, ACM Press, 1985. focuses on the local relationships between documents. The local interpretation avoids having to model the number or size of clusters in the collection and allow relevance at multiple scales. Methods in this spirit include,
      • multiple cluster retrieval
      • spreading activationS. Preece, A spreading activation network model for information retrieval. PhD thesis, University of Illinois, Urbana-Champaign, 1981. and relevance propagationT. Qin, T.-Y. Liu, X.-D. Zhang, Z. Chen, and W.-Y. Ma, “A study of relevance propagation for web search,” in SIGIR ’05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, (New York, NY, USA), pp. 408–415, ACM Press, 2005. methods
      • local document expansionA. Singhal and F. Pereira, “Document expansion for speech retrieval,” in SIGIR ’99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, (New York, NY, USA), pp. 34–41, ACM Press, 1999.
      • score regularizationF. Diaz, “Regularizing query-based retrieval scores,” Information Retrieval, vol. 10, pp. 531–562, December 2007.
    • Local methods require an accurate and appropriate document similarity measure.

2015

2007