7. A system for multimodal clue based personalized app function recommendation, comprising: a mobile clues module configured to obtain a plurality of real-time clues collected from a user; a user preference repository module configured to: extract 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; use 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 combine the two separate two-layer DBMs by adding an additional layer of binary hidden units on the top of the two-layer DBMs; and based on the plurality of extracted features, generate a joint representation of a multimodal clue; an app function recommendation module configured to create a logistic regression model based on the joint representation of the multimodal clue and rank a list of app functions using the logistic regression model; and an app functions module configured to display a ranked app function list for the user.
12. The system according to claim 7 , wherein: provided that v s εR D denotes a structured input, and h s (1) ,h s (2) ε{0,1} are binary stochastic hidden units, a probability that the structured feature two-layer DBM assigns to vertex v s is defined by: p ⁡ ( v s ; θ s ) = ∑ h s ( 1 ) , h s ( 2 ) ⁢ ⁢ p ⁡ ( v s , h s ( 1 ) , h s ( 2 ) ; θ s ) = 1 z ⁡ ( θ s ) ⁢ ∑ h s ( 1 ) , h s ( 2 ) ⁢ exp ⁡ ( - E ⁡ ( v s , h s ( 1 ) , h s ( 2 ) ; θ s ) ) wherein E ⁡ ( v s , h s ( 1 ) , h s ( 2 ) ; θ s ) = - ∑ i = 1 D ⁢ ( v si - b i ) 2 2 ⁢ σ i 2 + ∑ i = 1 D ⁢ ∑ j = 1 F 1 ⁢ v si σ i ⁢ W ij ( 1 ) ⁢ h j ( 1 ) + ∑ j = 1 F 1 ⁢ ∑ l = 1 F 2 ⁢ h j ( 1 ) ⁢ W jl ( 2 ) ⁢ h l ( 2 ) is an energy function, and θ s ={b,W (1) ,W (2) ,σ} are model parameters.