I'm currently learning about Collaborative Learning and Content-based Recommendation.One of the main things that is discussed in both methods is about calculating similarity between two users or two items. Commonly, similarity between two entity (each represented by a vector) can be calculated using Cosine similarity or other similarity coefficients.One question that I haven't found the answer is about calculating similarity between two entities in which each has a feature that explains each entity's location.Let's say, the entities in my data is about restaurants and each restaurant has several attributes such asfood price averageuser rating/review scorelocation in $(\text{lat}, \text{long})$etc.For food price average or user rating, maybe it's still relevant to include them in cosine similarity function, but how about locationwhich is described in a tuple of real numbers? What's the best way to include distance between entities as a component in the similarity function?Thanks!
Item Similarity with Location Feature
recommender system;similarity
Perhaps you could transform the latitude and longitude into spherical coordinates. In this coordinate system, the cosine of the angle between the vectors has a natural geometric interpretation.