Ranking search results using a single ranking function for all search engine visitors is inherently bounded in the performance the ranking algorithm can achieve when considering the variety of requirements of Web searchers and the proliferation of topics and types of data modern search engines rank. Adding a geographical dimension to the mix by way of local search engines further reduces the average satisfaction a ranking algorithm can garner from local search users. Personalization has been proposed in Web search with some success but has not, to our knowledge, been investigated thoroughly in local search. As initial steps in local search personalization, we propose a model for personalizing search results in a local search engine using a hybrid of profile- and click-based user modeling methods. User profiles are used to compare local search results to the topical interests of users and the specific businesses in which they have shown interest by way of search result “clicks”. Our model is tested through a user study and is shown to result in significantly improved mean average precision over the baseline ranking system.

Since the debut of PageRank and HITS, hyperlink-induced Web document ranking has come a long way. The Web has become increasingly vast and topically diverse. Such vastness has led many into the area of topic-sensitive ranking and its variants. We address the high dimensionality of the Web by providing tools for focused search. A focused search engine is one which seeks coverage over a subset of topics of the Web and presents users with relevant search results in a known domain. This demonstration will introduce readers to the GenieKnows.com Vertical Search Engine.

This paper introduces a novel link-based ranking algorithm based on a model of focused Web surfers. FocusedRank is described and compared to implementations of PageRank and Topic-Sensitive PageRank. We report a user study that measures the relevance and precision of each approach. FocusedRank gives superior relevancy over PageRank, while significantly reducing the computational complexity compared to the Topic-Senstivice PageRank.