A unified attractiveness prediction method is provided. The method includes receiving a plurality of videos and extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video. The method also includes obtaining potential view amounts of the plurality of received videos and calculating impact factor scores of the plurality of received videos based on the potential view amounts, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video. Further, the method includes providing a video with a highest impact factor score based on the calculated impact factor scores.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A unified attractiveness prediction method, comprising: receiving a plurality of videos; extracting at least one of metadata and view data from each of the plurality of received videos, wherein the metadata is information for describing video contents, and the view data is a total number of users who watch the video; obtaining potential view amounts of the plurality of received videos; based on the potential view amounts, calculating impact factor scores of the plurality of received videos, each impact factor score including a numerical score for indicating a degree of effectiveness of a corresponding video; and based on the calculated impact factor scores, providing a video with a highest impact factor score. 2. The method according to claim 1, further including: based on the extracted view data, performing a Hawkes process for the received videos associated with the view data to infer a plurality of parameters of the received videos; and based on the plurality of obtained parameters and features converted from the extracted metadata, creating a linear regression model and learning feature weight parameters from the created linear regression model. 3. The method according to claim 1, wherein: the plurality of obtained parameters include immigrant parameters and propagation parameters, wherein the immigrant parameters govern a total number of users who spontaneously watch the video, and the propagation parameters govern a total number of users who watch the video by others' recommendation. 4. The method according to claim 2, wherein creating a linear regression model of the received videos further includes: using the obtained immigrant parameters as dependent variables of the linear regression model; and using the features converted from the metadata as independent variables of the linear regression model. 5. The method according to claim 1, wherein: a plurality of nodes, including positive nodes and negative nodes, corresponding to the plurality of received videos compose the Hawkes processes, wherein each of the positive nodes composes a positive Hawkes process, and each of the negative nodes composes a negative Hawkes process. 6. The method according to claim 3, further including: when the videos include the metadata and the view data, performing the Hawkes process by using directly the view data to infer the immigrant parameters and the propagation parameters; when the videos only include the metadata, generating the immigrant parameters by using directly the feature weight parameters for the Hawkes processes, and ignoring the propagation part, such that the immigrant parameters are used to make a rough prediction for near future; and when the videos only include the view data, performing the Hawkes process directly and ignoring the immigrant part to make the rough prediction. 7. The method according to claim 6, wherein: a rate function of the Hawkes process is defined by: λi=μi(t)+Σti,k<tφii(t−tik)+Σtjl<tφii(t−tjl) wherein i, jε(1,2), and 1 indicates that the node comes from a positive queue, and 2 indicates that the node comes from a negative queue; t is a time point; μi(t) is an immigrant function; Σtl,k<tφii(t−tik) is a self-exciting function that measures how a previous event in the queue evokes other events happening in a same queue; and Σtjl<tφij(t−tjl) is a cross-exciting function that measures how events in one queue affect events in the other queue. 8. The method according to claim 1, wherein: provided that the most recent K time periods data is taken into consideration, a content impact factor score (CIFS) is calculated by: CIFS t = ∑ i = 1 K  V i , t ∑ i = 1 K  max  ( V i , * ) wherein Vi,t indicates a view amount of content t from the ith time period, and max (Vi,*) indicates a maximum view amount from the contents in the ith time period. 9. A system for making a unified attractiveness prediction, comprising: a bivariate Hawkes processing module configured to: extract view data from a plurality of received videos, wherein the view data is a total number of users who watch the video; and based on the extracted view data, perform a Hawkes process for the received videos associated with the view data to infer a plurality of parameters of the received videos; a potential view amount calculation module configured to obtain potential view amounts of the plurality of received videos; and an impact factor score calculation module configured to calculate impact factor scores of the plurality of received videos based on the potential view amounts and provide a video with a highest impact factor score based on the calculated impact factor score of each video, wherein each impact factor score includes a numerical score for indicating a degree of effectiveness of a corresponding video. 10. The system according to claim 9, further including: a parameter inference module configured to receive the plurality of parameters inferred from the Hawkes process; a liner regression module configured to create a linear regression model based on the plurality of obtained parameters and features converted from metadata of the received videos, wherein the metadata is information for describing video contents; and a feature weight parameter module configured to learn feature weight parameters from the created linear regression model; 11. The system according to claim 10, wherein: the plurality of obtained parameters include immigrant parameters and propagation parameters, wherein the immigrant parameters govern a total number of users who spontaneously watch the video, and the propagation parameters govern a total number of users who watch the video by others' recommendation. 12. The system according to claim 10, wherein the liner regression module is further configured to: use the obtained immigrant parameters as dependent variables of the linear regression model; and use the features converted from the metadata extracted from the plurality of received videos as independent variables of the linear regression model. 13. The system according to claim 9, wherein: a plurality of nodes, including positive nodes and negative nodes, corresponding to the plurality of received videos compose the Hawkes processes, wherein each of the positive nodes composes a positive Hawkes process, and each of the negative nodes composes a negative Hawkes process. 14. The system according to claim 10, wherein: when the videos include the metadata and the view data, the Hawkes process is performed by using directly the view data to infer the immigrant parameters and the propagation parameters; when the videos only include the metadata, the immigrant parameters is generated by using directly the feature weight parameters for the Hawkes processes, and the propagation part is ignored, such that the immigrant parameters are used to make a rough prediction for near future; and when the videos only include the view data, the Hawkes process is performed directly, and the immigrant part is ignored, making the rough prediction. 15. The system according to claim 14, wherein: a rate function of the Hawkes process is defined by: λi=μi(t)+Σti,k<tφii(t−tik)+Σtjl<tφij(t−tjl) wherein i, jε(1,2), and 1 indicates that this node comes from a positive queue, and 2 indicates that this node comes from a negative queue; t is a time point; μi(t) is an immigrant function; Σtl,k<tφii(t−tik) is a self-exciting function that measures how a previous event in the queue evokes other events happening in a same queue; and Σtjl<tφij(t−tjl) is a cross-exciting function that measures how events in one queue affect events in the other queue. 16. The system according to claim 9, wherein: provided that the most recent K time periods data is taken into consideration, a content impact factor score (CIFS) is calculated by: CIFS t = ∑ i = 1 K  V i , t ∑ i = 1 K  max  ( V i , * ) wherein Vi,t indicates a view amount of content t from the ith time period, and max (Vi,*) indicates a maximum view amount from the contents in the ith time period.