an approach to use query-related web context on document ranking
With the development of Web search engines, it is considered as an important task to provide retrieved documents in a proper manner. Many search engines have used various document ranking algorithms to provide their retrieved documents in a more efficient way for users. However, even though a good algorithm is used, there are some limitations if they do not consider the characteristic of queries which is diverse depending on user intention or interest. Even if a user searches documents with the same query, he/she may want a different result depending on when he/she queries into a search engine. How can a search engine judge what way is more efficient to provide retrieved results? We suggest a simple and novel way which employs query-related Web context to answer this question. With the distribution of query-related tweets and news articles, we classify whether a query would be considered as a hot query or a cold query . And then, we extract major topic terms from the hot time slice if a query is classified as a hot query , or extract refined contents if a query is classified as a cold query . Finally, all retrieved results are re-ranked by reflecting these topic terms or refined contents according to the characteristic of the query. To show the meaningfulness of our approach, we compare our re-ranked results with original retrieved results from the commercial search engine.
web context document ranking web search twitter web page ranking data mining query classification