Personalization of Content Ranking in the Context of Local Search

Abstract

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.