- Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”. Cambridge University Press. 2008. Chapter 9.
- Xu, J. and Croft, W. B. (2000). Improving the effectiveness of information retrieval with local context analysis. ACM Trans. Inf. Syst.18, 1 (Jan. 2000), 79-112. (DOI= http://doi.acm.org/10.1145/333135.333138)
- Wang, X., Fang, H., and Zhai, C. (2008). A study of methods for negative relevance feedback. In Proceedings of the 31st Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Singapore, Singapore, July 20 - 24, 2008). SIGIR '08. ACM, New York, NY, 219-226. (DOI= http://doi.acm.org/10.1145/1390334.1390374)
- Donna Harman, (1992). Relevance feedback revisited. Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval. Pages: 1 - 10. Copenhagen, Denmark. 1992. (http://doi.acm.org/10.1145/133160.133167)
From 1:
I have never seen a search engine with a relevance feedback system. I wonder if there are any practical examples? ... Oh, ok Excite used it primarily as a way to link people to more results like a certain result.
The weakness of the Rocchio algorithm is that it requires relevant documents to cluster into a single group. Any factor that causes the vector-space model to breakdown or cause relevant documents for form multiple clusters decrease the effectiveness of this relevance feedback algorithm.
Query reformulation, especially for technical queries is a way to make the user's query more likely to return relevant results.
From 3:
It is more difficult to use negative relevance feedback to improve results than positive relevance feedback. However, negative feedback works better in the language model than a vector-space model. Most helpful for very difficult queries with poor results.
From 4:
They found that query expansion should be used with relevance reweighting with most of the gains coming from query expansion.
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