11. A method for content recommendation, comprising: accessing electronic data from multiple different sources, where the electronic data represents unstructured text in two or more different languages, where the unstructured text represents titles or descriptions for items available in an electronic marketplace; extracting one or more features from the data; producing a plurality of feature vectors from the one or more features where a feature vector comprises one or more elements; producing from the plurality of feature vectors, one or more feature spaces from which a content-based similarity recommendation can be made; producing a graph with nodes and edges where the nodes represent items available in an electronic marketplace and the edges represent content-based similarity relationships, where the content-based similarity relationships are based, at least in part, on the one or more feature spaces, wherein the graph is represented using a latent vector space model that provides a distance function between nodes in the graph; and producing a content-based similarity score for two nodes that are not directly connected by an edge in the graph, where the content-based similarity score is a function of two or more other content-based similarity scores computed for other pairs of nodes in the graph.
16. The method of claim 11 , cleaning the electronic data to make clean data from which feature vectors can be produced.