Efficient Diversification of Web Search Results
- Day - Time: 07 June 2011, h.11:00
- Place: Area della Ricerca CNR di Pisa - Room: C-29
In this paper we analyze the efficiency of various search re- sults diversification methods. While efficacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unified framework for studying performance and feasibility of result diversification solutions is thus proposed. First we define a new methodology for detecting when, and how, query results need to be diversified. To this purpose, we rely on the concept of “query refinement” to estimate the probability of a query to be ambiguous. Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three different diversifica- tion methods: IASelect, xQuAD, and OptSelect. While the first two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evalu- ate both the efficiency and the effectiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effectiveness. This seminar will present results from the paper "Efficient Diversification of Web Search Results" that will be presented at the upcoming VLDB conference in Seattle. This is joint work with: Gabriele Capannini, Franco Maria Nardini, and Raffaele Perego.