Rank quantization

Abstract

We study the problem of aggregating and summarizing partial orders, on a large scale. Our motivation is two-fold: to discover elements at similar preference levels and to reduce the number of bits needed to store an element's position in a full ranking. We proceed in two steps: first, we find a total order by linearizing the rankings induced by the multiple partial orders and removing potentially inconsistent pairwise preferences. Next, given a total order, we introduce and formalize the rank quantization problem, which intuitively aims to bucketize the total order in a manner that mostly preserves the relations appearing in the partial orders. We show an exact quadratic-time quantization algorithm, as well as a greedy 2 /3-approximation algorithm whose running is substantially faster on sparse instances. As an application, we aggregate rankings of top-10 search results over millions of search engine queries, approximately reproducing and then efficiently encoding the underlying static ranks used by the engine. We evaluate the performance of our algorithms on a web dataset of 12 million (2 23.5 ) pages and show that we can quantize the pages' static ranks using as few as eight bits, with only a minor degradation in search quality.

Data Mining in Web Search Engine Optimization and User Assisted Rank Results

“…content mining as agentbased approaches; where intelligent web agents such as crawlers autonomously crawl the web and classify data and database approaches; where information retrieval tasks are employed to store web data in databases where data mining process can take place Most web content mining studies have focused on textual and graphical data since the early years of internet mostly featured textual or graphical information. Recent studies started to focus on visual and aural data such as sound and video content too [2,3].…”