Coping with Noisy Search Experiences

Abstract

The so-called social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. User-generated content, from blogs and wikis to ratings and comments, all add an important layer of experiential knowledge to our online interactions. In this paper we describe an approach to improving mainstream Web search by harnessing the search experiences of groups of likeminded searchers. We focus on the HeyStaks system (www.heystaks.com) and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature.Experience is the name everyone gives to their mistakes.-Oscar Wilde

Approximating web communities using subspace decomposition

“…Recommendation systems [35,39,8,32], harvest user search experiences and suggest appropriate search results. However, recommendation systems, are often influenced by noise-links as well (depending on the algorithm that is implemented).…”

“…Similarly, Champin et al [8] proposed a link-based method to improve search results in HeyStaks 1 recommendation system. The technique shows satisfactory results in countering spam, however, the precise contribution to the score of authoritative webpages, is unknown.…”

“…With reference to Eqs. (8) and (5), we define following conditions as in [18,13]: Letr ns be singular values in the leading diagonal of D.…”

An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

“…Due to the rapid increase in the need for efficiently and effectively organizing and utilizing the data that are in huge volume, intelligent search/query techniques play a more and more important role nowadays for supporting customers and companies decisions [56,57,61]. For example, an online recommendation system based on such intelligent techniques may provide the customers with a service that links a purchase intention (e.g., via search keywords) with relevant products or advertisements.…”