7. A computer-readable storage medium storing computer-executable instructions that when executed by a computer control the computer to perform a method, the method 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 applications available in an electronic marketplace; cleaning the electronic data to make clean data from which feature vectors can be produced, where cleaning the electronic data comprises performing one or more processes independently or interdependently, where the one or more processes change the capitalization of a word in the electronic data, separate concatenated words in the electronic data, merge synonyms in the electronic data, remove a non-Unicode symbol in the electronic data, remove a banned word in the electronic data, remove an uninformative word in the electronic data, or translate a word in the electronic data; extracting one or more features from the cleaned data using tokenization, n-gram extraction, proper noun detection, lemmatization, or stemming; producing weights for members of the multiple different data sources, for different types of data, for different types of features, or for different features; producing scores for the one or more features based, at least in part, on the weights and on term frequency—inverse document frequency (TF-IDF) or latent semantic indexing; producing a plurality of feature vectors from the one or more features based, at least in part, on the scores, where a feature vector comprises one or more elements, the one or more elements being single words, types of nouns, n-grams, short phrases, symbols, acronyms, or abbreviations; producing from the plurality of feature vectors, one or more feature spaces from which a content-based similarity recommendation can be made, where the one or more feature spaces are associated with single languages, where the one or more feature spaces depend, at least in part, on the weights; producing a graph whose nodes represents the applications available in the electronic marketplace and whose 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, 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.
7. A computer-readable storage medium storing computer-executable instructions that when executed by a computer control the computer to perform a method, the method 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 applications available in an electronic marketplace; cleaning the electronic data to make clean data from which feature vectors can be produced, where cleaning the electronic data comprises performing one or more processes independently or interdependently, where the one or more processes change the capitalization of a word in the electronic data, separate concatenated words in the electronic data, merge synonyms in the electronic data, remove a non-Unicode symbol in the electronic data, remove a banned word in the electronic data, remove an uninformative word in the electronic data, or translate a word in the electronic data; extracting one or more features from the cleaned data using tokenization, n-gram extraction, proper noun detection, lemmatization, or stemming; producing weights for members of the multiple different data sources, for different types of data, for different types of features, or for different features; producing scores for the one or more features based, at least in part, on the weights and on term frequency—inverse document frequency (TF-IDF) or latent semantic indexing; producing a plurality of feature vectors from the one or more features based, at least in part, on the scores, where a feature vector comprises one or more elements, the one or more elements being single words, types of nouns, n-grams, short phrases, symbols, acronyms, or abbreviations; producing from the plurality of feature vectors, one or more feature spaces from which a content-based similarity recommendation can be made, where the one or more feature spaces are associated with single languages, where the one or more feature spaces depend, at least in part, on the weights; producing a graph whose nodes represents the applications available in the electronic marketplace and whose 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, 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. 8. The media of claim 7 , where the graph is represented using a latent vector space model, where the graph or latent vector space model provides a distance function between items and items or between items and users.