A Quadratic Lower Bound for Rocchio's Similarity-Based Relevance Feedback Algorithm

Rocchio’s similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. As a first step towards formal analysis of Rocchio’s algorithm, it is shown in [4] that Rocchio’s algorithm makes Ω(n) mistakes in searching for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space {0, 1}n . In practice, Rocchio’s algorithm often uses a fixed query updating factor and a fixed classification threshold. When this is the case, we strengthen the work in [4] in this paper and prove that Rocchio’s algorithm makes Ω(k(n − k)) mistakes in searching for a collection of documents represented by a monotone disjunction of k relevant featu...

• Type: Conference
• Year: 2005
• Where: COCOON
• Authors: Zhixiang Chen, Bin Fu
• Keywords: Algorithm, Binary Vector Space, COCOON 2005, Rocchio’s Similarity-based Relevance