אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Geographic search, where the user provides keywords and receives relevant locations depicted on a map, is a popular web application. Typically, such a search is based on static geographic data. However, the abundant geotagged posts in microblogs such as Twitter and in social networks like Instagram provides contemporary information that can be used to support geosocial searches. Geographic searches based on user activities in social media. Such searches can point out where people talk (or tweet) about different topics. For example, the search results may show where people refer to "jogging", to indicate popular jogging places. The difficulty of implementing such a search is that there is no natural partition of the space into "document" as in ordinary web searches. Thus, it is not always clear how to present results and how to rank and filter results effectively. In this thesis, we demonstrate a two-step process of first, quickly finding the relevant areas by using an arbitrary indexed partition of the space, and second, by applying clustering techniques on discovered areas to present more accurate results. We introduce a framework that utilizes geotagged posts in geographic searches and illustrate how different ranking methods can be used based on the proposed two-step search process. The framework demonstrates the effectiveness and usefulness of the approach.