A system for self-adaptive user reading preference learning and screen layout optimization for multi-media information is disclosed. The system is particular useful for devices with small display form factors, such as mobile devices. Data flow diagram (DFD) technique is used to represent a model of the screen layout for visualization. The system processes input information, which includes user information, device information, context information, and the news. The delivery news is filtered in the news processing system and presented to a user based the user's interest, whereas device information and context information, are also factors that take effect in the modeling.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method performed by a computer system to provide screen layout based on user preference topics comprising: providing a plurality of N information documents to a user; providing a user profile of the user, wherein the user profile includes the user's preferences, wherein the user profile contains user information, context information and device information; pre-processing the information documents based on the user's profile to form N pre-processed information documents; calculating relevance of the N pre-processed information documents to determine similarity to the user profile; sorting the N pre-processed information documents in descending order of similarity; presenting the N pre-processed information documents based on descending order of similarity to the user; performing self-adaptive learning; and updating the user profile based on the self-adaptive learning. 2. The computer-implemented method of claim I wherein N information documents comprises N news documents. 3. The computer-implemented method of claim I wherein N information documents comprise text type, image type, audio and video type and mixed media type or a combination thereof. 4. The computer-implemented method of claim 3 wherein pre-processing comprises: categorizing the information documents based on document type; and pre-processing the information documents based on document type. 5. The computer-implemented method of claim 4 wherein: pre-processing the text type information document comprises compressing text information, resizing according to device information, adjusting font and page breaks based on the user information or a combination thereof; pre-processing the image type information document comprises resizing the image based on the device information, reducing margins between the image and associated text, compressing the associated text, adjusting the font based on the user information or a combination thereof; pre-processing the audio and video type information document comprises compressing audio and video using audio/video compression which include granularity tuning based on the user information; and pre-processing the mixed media type information document comprises combination of text, image and audio and video pre-processing including position arrangement of the different media. 6. The computer-implemented method of claim I wherein the user profile (P) comprises: P=(W1, W2, . . . , Wv), where V is the number of context factors, and Wv is a profile weight vector; and Wv=(w1,v, s2,v, . . . , wd,v), where v is the index of the user profile, which is determined by context factors, w1,v is the weight of iith term in profile vector Wv, d is equal to the number of terms in the user profile, and Wv is normalized to 1 (∥Wv∥=1). 7. The computer-implemented method of claim 6 wherein the N information documents are represented by N document vectors, wherein a document vector comprises Xk=(x1,k, x2,k, . . . , xd,k,), where k is to the kth document retrieved, and k is from 1 to N, xi,k is the weight of ith term in document vector Xk, and Xk is normalized to 1 (∥Xk∥=1). 8. The computer-implemented method of claim 7 wherein the relevance comprises: V p , k = W · X = ∑ j = 1 d   w j , p  x j , k where j is the jth term of vectors W and X. 9. The computer-implemented method of claim 1 wherein the self-adaptive learning comprises: observing actions of the user to get feedback; and updating the user profile based on the feedback. 10. The computer-implemented method of claim 1 wherein the self-adaptive learning employs a Q learning function comprising: Q  ( s , a ) = r + γ  max a ′  Q  ( s ′ , a ′ ) where s is the state defined as a pair of the user profile and the news to be filtered, a is defined as the action of presenting a specific news to the user, r is the immediate reward taking action a at state s, y is the discount factor 0≦γ<1, s′ is the resulting state, and a′ is the candidate action on state s′. 11. The computer-implemented method of claim 10 wherein the Q learning function employs an E-greedy strategy which chooses action a=π(s) for state s comprising: π  ( s ) = { arg   max a  Q ^  ( s , a ) , p > ɛ a sn   or   a rand , p < ɛ where p is the random value uniformly distributed in [0, 1], ε the defined parameter, asn is the action selecting a news from a social network, and arand indicates selecting a low rank news randomly when there is no available news from the social network. 12. The computer-implemented method of claim 11 wherein Q ^ t + 1  ( s , a ) = ( 1 - α )  Q ^ t  ( s , a ) + α  [ r t + γ   max a ′  Q ^ t  ( s ′ , a ′ ) ] where α = 1 1 + visit  ( s , a ) is the learning rate, and r is the immediate reward for action a carried out in state s. 13. The computer-implemented method of claim 12 wherein r takes into account the user's behavior B which comprises: average reading time for one word rt, following a hyperlink fl, a search word sw, bookmarking bm, saving page sp, and scrolling sc, or a combination thereof. 14. The computer-implemented method of claim 13 wherein r = ∑ v ∈ B   f v  b v . 15. The computer-implemented method of claim 14 wherein updating the user profile comprises: w i , p = w l , p + ∑ k   β   r k  I  ( x l , k ) , where β is the learning rate, k is the overall presented news, and I(xi,k) is the threshold function for the contribution of news k to feature term i. 16. A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer to determine sentiments of users for topics comprising: retrieving a user profile of the user, wherein the user profile includes the user's preferences, wherein the user profile contains user information, context information and device information; setting t=0; initializing a Q table having entries (s, a) to 0 for each s and a, where s is a state and a is an action; and do forever retrieving a plurality of N information documents from the user, pre-processing the information documents based on the user's profile to form N pre-processed information documents, calculating relevance of the N pre-processed information documents to determine similarity to the user profile, sorting the N pre-processed information documents in descending order of similarity, initializing s, repeat until all s are processed, select an action a, receive immediate reward r, update Q table for s and a, and s<-s′, presenting first M news, getting feedback by observing behaviors, and updating the user profile. 17. A computer system for determining sentiments of users for topics comprising: an input module, the input module includes an information unit having a plurality of N information documents from a user, and a user profile of the user, wherein the user profile includes the user's preferences, wherein the user profile contains user information, context information and device information; a processing module, the processing module includes a pre-processor unit for pre-processing the information documents based on the user's profile to form N pre-processed information documents, and a processor unit, the processor unit calculates relevance of the N pre-processed information documents to determine similarity to the user profile, and sorts the N pre-processed information documents in descending order of similarity; and an output module, the output module presents the N pre-processed information documents based on descending order of similarity to the user. 18. The computer system of claim 17 wherein the processor module comprises: a learning unit for performing self-adaptive learning; and an update unit for updating the user profile based on the self-adaptive learning. 19. The computer system of claim 18 wherein: the learning unit observes actions of the user to get feedback; and the update unit updates the user profile based on the feedback. 20. The computer system of claim 18 wherein the self-adaptive learning unit employs a Q learning function comprising Q  ( s , a ) = r + γ  max a ′  Q  ( s ′ , a ′ ) where s is the state defined as a pair of the user profile and the news to be filtered, a is defined as the action of presenting a specific news to the user, r is the immediate reward taking action a at state s, y is the discount factor 0≦γ<1, s′ is the resulting state, and a′ is the candidate action on state s.