Map data is more complex and critical than sales or social data. However, its one advantage over the others, is that it is mostly static in nature. So what kind of recommendations can we generate for map data? Suppose a user searches for a location on the map, you can generate suggestions for nearby places of interest depending upon the user's search history. For example, if a user searches for restaurants once in a while, you could generate suggestions for restaurants in any new locations that he visits. Let us look at how to approach this issue.

Consider a map data set which represents the locations in the form of nodes, and the roads connecting them in the form of bi-directional relationships. The Location entity and its properties can be illustrated as follows

The Road entity and its property structure can be illustrated in the web interface as follows:

So, the map graph by itself is not sufficient to create a recommendation system. We will need to add it to scenarios involving user specific data, such as location based searches (advertisements or establishments to be recommended based on location and proximity of the user) or favorable logistic paths (delivery of goods through cab services is a very good example, where a cabbie can deliver goods to a location if he is already headed that way. Hence, a recommender system can be devised to suggest delivery locations to cabbies based on the areas they frequent or routes they take. The possibilities are endless when you can reference a map graph from a social or transport graph. So you combine the social graph and the map graph given above, by linking a person from the first to his corresponding locality in the second. This dual layered graph will now allow you to operate on the map, when you are simultaneously traversing the social graph.

A similar approach can be taken for airlines or logistic graphs, where the map component of the graph and the chief operation can be logically segregated, but physically linked. Now you can devise your own recommendation algorithms to consider location info for generating suggestions. Since you have now used pattern matching to generate recommendations for social data, we leave for you an exercise to devise a recommender for maps, according to any of the scenarios discussed earlier.