With the increasing popularity of sharing media on social networks and facilitating access to location technologies, such as Global Positioning System (GPS), people are more interested to share their own photos and videos. The world wide web users are no longer the sole consumer but they are producers of information also, hence a wealth of information are available on web 2.0 applications. The shared media usually contain geo-tagged locations, time stamp, hashtags, and comments. As such, mining social networks can yield extensive knowledge about human dynamics and mobility behaviors within urban context So web users are no longer just users but also producers of information This wealth of information can be leveraged for location-based services. If the locations visited by users are collected and sorted according to the timestamps, the sequence that the user has visited can be determined; exploring of which can be used in tourism planning. Recently, there is an increasing tendency to adopt the information from these geo-tagged photos for learning to recommend tourist locations. For a tourist, before traveling to an unfamiliar city, the most important preparation is planning the trip. without any prior knowledge, tourist must either rely on travel books, personal travel blogs or combination of online resources and services. It is difficult and time consuming and painstaking to find out the locations worth to visit and figure out the order in which they are to be visited. Hence, the purpose of the present study is to provide a framework for recommending locations and travel sequences to tourists by using geo-tagged photos in social networks. Most existing methods for tourist recommendation do not consider context constraints, or at best, address a few dimensions of contexts. The present work aims to develop a context-aware recommender system that recommends interesting locations and the travel sequence. The proposed method is designed such that it can use the collective wisdom of people from collection of geo-tagged photos in order to provide a set of tourism locations and interesting trip sequences that matches the user's current context given a city that is unfamiliar to that user.  first Due to the low accuracy of positioning with GPS embedded in mobile phones to find a unique pair of geographic coordinates for a tourist place the geo-tagged photos were clustered. For this reason OPTICS clustering method exploit to group geo-tagged photos by their locations. It then uses the combined method, to annotate all of the clusters that are created in the previous step with semantics. Then, we create a profile for clusters by using historical context (time stamps and weather). After that, we generated a travel sequences database and rated the sequences in the database according to their context. Finally In order to evaluate the performance of the proposed method, Panoromia site dataset of one region in Tehran was used and Experimental results showed that 64.5% of the results obtained by our proposed strategy are identical with the user preferences, which illustrate rationality of the recommendation from analyzing the geo-tagged photos.