Discovering Implicit Feedbacks from Search Engine Log Files

Abstract

A number of explicit and implicit feedback mechanisms have been proposed to improve the quality of the search engine results. The current approaches to information retrieval depends heavily on the web linkage structure which is a form of relevance judgment by the page authors. However, to overcome spamming attempts and the huge volumes of data, it is important to also incorporate the user feedback on the page relevance of a document. Since users hardly give explicit/direct feedback on search quality, it becomes necessary to consider implicit feedback that can be collected from search engine logs. In this article we evaluate two implicit feedback measures, namely click sequence and time spent in reading a document. We develop a mathematical programming model to collate the feedback collected from different sessions into a partial rank ordering of documents. The two implicit feedback measures, namely the click sequence and time spent in reading a document are compared for their feedback information content using Kendall’s τ measure. Experimental results based on actual log data from AlltheWeb.com demonstrate that these two relevance judgment measures are not in perfect aggrement and hence incremental information can be derived from them.