13. A non-transitory computer-readable medium having computer program for, when being executed by a processor, performing a method for multimodal clue based personalized app function recommendation, the method comprising: receiving an app search query from a user; obtaining a plurality of real-time clues collected from the user; extracting a plurality of features to represent the plurality of real-time clues collected from the user, wherein the plurality of features include structured features and text features; based on the plurality of extracted features, generating a joint representation of a multimodal clue, comprising: using a multimodal Deep Boltzmann Machine (DBM) to construct two separate two-layer DBMs to model distribution over the structured features and the text features respectively; and combining the two separate two-layer DBMs by adding an additional layer of binary hidden units on the top of the two-layer DBMs; based on the joint representation of the multimodal clue, creating a logistic regression model; ranking a list of app functions using the logistic regression model; and displaying a ranked app function list for the user.
13. A non-transitory computer-readable medium having computer program for, when being executed by a processor, performing a method for multimodal clue based personalized app function recommendation, the method comprising: receiving an app search query from a user; obtaining a plurality of real-time clues collected from the user; extracting a plurality of features to represent the plurality of real-time clues collected from the user, wherein the plurality of features include structured features and text features; based on the plurality of extracted features, generating a joint representation of a multimodal clue, comprising: using a multimodal Deep Boltzmann Machine (DBM) to construct two separate two-layer DBMs to model distribution over the structured features and the text features respectively; and combining the two separate two-layer DBMs by adding an additional layer of binary hidden units on the top of the two-layer DBMs; based on the joint representation of the multimodal clue, creating a logistic regression model; ranking a list of app functions using the logistic regression model; and displaying a ranked app function list for the user. 15. The non-transitory computer-readable medium according to claim 13 , wherein ranking the list of app functions using the logistic regression model further includes: calculating app scores to filter out irrelevant app functions; and based on the calculated app scores, ranking the list of relevant app functions that are scored.