Patent Publication Number: US-2018046724-A1

Title: Information recommendation method and apparatus, and server

Description:
The present application is a continuation of International Patent Application No. PCT/CN2016/113895 filled on Dec. 30, 2016, which claims priority to Chinese Patent Application No. 201610019783.7, titled “INFORMATION RECOMMENDATION METHOD AND APPARATUS, AND SERVER”, filed on Jan. 12, 2016 with the State Intellectual Property Office of People&#39;s Republic of China, both of which are incorporated herein by reference in their entireties. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to the technical field of information processing, and in particular to an information recommendation method, an information recommendation apparatus, and a server. 
     BACKGROUND 
     With development of social applications, pushing recommended information, such as an advertisement and weather information, via the social application becomes a new approach for information service to provider recommend information to users. Similar to sharing information among one or more friends in the social application, the user can interact, such as making comments or giving the thumbs-up, with the recommended information such as the advertisement and the weather information pushed by the social application. To effectively push the recommended information, it is important to estimate probability that the user interacts, such as making comments or giving the thumbs-up, with the recommended information after the recommended information is pushed to the user. If the probability that the user interacts with the recommended information is higher, the interaction effect on the pushed recommended information is better. 
     At present, in pushing recommended information, normally an interest level of the user in the recommended information is measured based on a relevance degree between the user and the recommended information. If the interest level of the user on the recommended information is higher, the possibility that the user interacts with the recommended information is higher. Therefore, whether to push the recommended information to the user is determined on the basis of the interest level of the user on the recommended information. 
     SUMMARY 
     In view of this, an information recommendation method, an information recommendation apparatus, and a server are provided according to embodiments of the present disclosure. 
     To achieve the above objective, the following technical solutions are provided according to an embodiment of the present disclosure. 
     An information recommendation method is provided, which includes: 
     determining a target friend who has interacted with target recommended information among one or more friends of a target user; 
     determining data of interaction made by the target user with previously shared information published by the target friend; 
     determining an influence degree of the target friend on interaction to be made by the target user with the target recommended information based on the data of interaction made by the target user with the previously shared information published by the target friend; 
     determining a target influence degree based on the influence degree of the target friend on the interaction to be made by the target user with the target recommended information; 
     determining a probability degree of the interaction to be made by the target user with the target recommended information based on the target influence degree; and 
     pushing the target recommended information to the target user, in a case that the probability degree meets a preset condition. 
     An information recommendation apparatus is further provided according to an embodiment of the present disclosure, which includes one or more processors and storage medium storing an operation instruction. the processor is configured to execute the operation instruction stored in the storage medium to perform following steps: 
     determining a target friend who has interacted with target recommended information among one or more friends of a target user; 
     determining data of interaction made by the target user with previously shared information published by the target friend; 
     determining an influence degree of the target friend on interaction to be made by the target user with the target recommended information based on the data of interaction made by the target user with the previously shared information published by the target friend; 
     determining a target influence degree based on the influence degree of the target friend on the interaction to be made by the target user with the target recommended information; 
     determining a probability degree of the interaction to be made by the target user with the target recommended information based on the target influence degree; and 
     pushing the target recommended information to the target user, in a case that the probability degree meets a preset condition. 
     A server is further provided according to an embodiment of the present disclosure, which includes the above information recommendation apparatus. 
     In the above technical solutions, based on a discovery that the rule of interaction to be made by a user with the shared information published by a friend is relevant to an influence of the friend on interaction to be made by the user with recommended information, in an embodiment of the present disclosure, the data of interaction made by the target user with the previously shared information published by the target friend is determined for the target friend among the one or more friends of the target user, who has interacted with the target recommended information. Then the influence degree of the target friend on the interaction to be made by the target user with the target recommended information is determined. Then the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information is integrated, to determine the target influence degree of the friend who has interacted with the target recommended information, on the interaction to be made by the target user with the target recommended information. The probability degree of the interaction to be made by the target user with the target recommended information is determined based on the target influence degree, for pushing the target recommended information. Because the influence degree of the friend on the interaction to be made by the target user with the target recommended information is referred to in determining the probability degree of the interaction to be made by the target user with the target recommended information in an embodiment of the present disclosure, accuracy of the determined probability that the user interacts with the recommended information is increased, so as to improve the effectiveness of pushing the recommended information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flowchart of an information recommendation method according to an embodiment of the present disclosure; 
         FIG. 2  is a flowchart of a method for determining an interaction weight and a preset constant according to an embodiment of the present disclosure; 
         FIG. 3  is a flowchart of another information recommendation method according to an embodiment of the present disclosure; 
         FIG. 4  is a flowchart of yet another information recommendation method according to an embodiment of the present disclosure; 
         FIG. 5  is a schematic diagram of relationships in a circle of friends according to an embodiment of the present disclosure; 
         FIG. 6  is a block diagram of an information recommendation apparatus according to an embodiment of the present disclosure; 
         FIG. 7  is a block diagram of an influence degree determining module according to an embodiment of the present disclosure; 
         FIG. 8  is a block diagram of a linear calculation unit according to an embodiment of the present disclosure; 
         FIG. 9  is a block diagram of another information recommendation apparatus according to an embodiment of the present disclosure; 
         FIG. 10  is a block diagram of a target influence degree determining module according to an embodiment of the present disclosure; 
         FIG. 11  is another block diagram of a target influence degree determining module according to an embodiment of the present disclosure; and 
         FIG. 12  is a hardware block diagram of yet another information recommendation apparatus according to an embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     After a friend of a user interacts with recommended information, a probability that the user interacts with the recommended information is increased. In a case that a certain type of interaction (such as making comments or giving the thumbs-up) is made by the friend of the user with the recommended information, a probability that the same type of interaction is made by the user with the recommended information is increased. 
     On the basis of the above, with an information recommendation method according to an embodiment of the present disclosure, the recommended information is pushed based on the rule that an interaction to be made by the target user with the recommended information is influenced by the interaction previously made by the friend of the target user with the recommended information, thereby increasing accuracy of determined probability that the user interacts with the recommended information, and improving effectiveness of pushing the recommended information. 
     The technical solutions according to embodiments of the present disclosure will be described clearly and completely in conjunction with the drawings in embodiments of the present closure. Apparently, the described embodiments are only a part of the embodiments according to the present disclosure, rather than all the embodiments. Any other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure fall within the scope of protection of the present disclosure. 
       FIG. 1  is a flowchart of an information recommendation method according to an embodiment of the present disclosure. The method may be applied to a server, where the server may collect user behavioral data of a social application, analyze and process the data, and push the recommended information. 
     Referring to  FIG. 1 , the information recommendation method provided according to an embodiment of the present disclosure may include step S 100  to step S 150 . 
     In step S 100 , a target friend who has interacted with target recommended information among one or more friends of a target user is determined. 
     The target recommended information is information to be recommended to the target user. In an embodiment of the present disclosure, the target recommended information has been pushed to at least one friend of the target user, while has not been pushed to the target user. A target friend among the at least one friend has interacted (such as making a comment or giving the thumbs-up) with the target recommended information. 
     In step S 110 , data of interaction made by the target user with previously shared information published by the target friend is determined. 
     The social application provides a function of sharing information among friends, with which a user can share information, such as an article and music, with the friend of the user, and the friend can make interaction, such as making comments, forwarding, or giving the thumbs-up, with the information shared by the user. Similarly, the user can also interact with information shared by the friend of the user. 
     In an embodiment of the present disclosure, for a target friend, interaction made by the target user with the previously shared information published by the target friend in a preset time period may be analyzed to obtain the interaction data. 
     In an embodiment of the present disclosure, for each determined target friend, the number of interactions of each preset type made by the user with the previously shared information published by the target friend in a preset time period may be analyzed. The interactions of a preset type may refers to an interactive operation performed on the shared information by the user such as giving the thumbs-up and making a comment, which may be determined based on actual usage requirements. Therefore, according to an embodiment of the present disclosure, the number of interactions of a preset type can serve as an eigenvector, and each of the eigenvectors is collected to obtain an eigenvector set, which serves as the data of interaction made by the target user with the previously shared information published by the target friend. 
     In an embodiment of the present disclosure, for each determined target friend, the number of interactions of each preset type made by the user with the previously shared information published by the target friend in a preset time period, may be analyzed to obtain the integrated number of interactions by integrating the number of interactions of each preset types. The integrated number of interactions serves as the data of interaction made by the target user with the previously shared information published by the target friend. 
     In step  120 , an influence degree of the target friend on interaction to be made by the target user with the target recommended information is determined based on the data of interaction made by the target user with the previously shared information published by the target friend. 
     On the basis of a discovery that the interaction made by the friend of the user with the recommended information leads to a high probability that the user interacts with the recommended information, according to an embodiment of the present disclosure, the rule that an interaction to be made by the user with the recommended information is influenced by the interaction previously made by the friend of the user with the recommended information, may be quantified as the influence degree of the target friend on the interaction to be made by the target user with the target recommended information, and the rule of interaction made by the user with the shared information published by the friend is quantified as the data of interaction made by the target user with the previously shared information published by the target user. 
     Then, based on a pre-analyzed functional relationship between a influence degree of the friend on the interaction to be made by the user with the recommended information, and data of interaction made by the user with the previously shared information published by the friend, the influence degree of the target friend on the interaction to be made by the target user with the target recommended information is determined for each of the target users, based on the interaction data and the pre-analyzed functional relationship. 
     In an embodiment of the present disclosure, by analyzing history behavior data, it is found that the rule of interaction to be made by the target user with the shared information published by the friend, has an extremely high similarity with the rule of interaction made by the target user with the recommended information after being influenced by the interaction made by the friend of the target user interacts with the recommended information, which shows a linear relationship. That is, the rule of interaction made by the target user with the recommended information after being influenced by the interaction already made by the friend of the target user with the recommended information has a linear relationship with the rule of interaction to be made by the target user with the shared information published by the friend. Therefore, for each of the target friends, after obtaining the data of interaction made by the target user with the previously shared information published by the target friend, according to an embodiment of the present disclosure, the influence degree of the target friend on the interaction to be made by the target user with the target recommended information can be calculated based on the linear relationship. 
     The linear relationship, which is between the rule of interaction made by the user with the recommended information after being influenced by the interaction made by the friend of the user with the recommended information and the rule of interaction to be made by the user with the shared information published by the friend, is only an example of the functional relationship mentioned above. In practice, the relationship may also be other functional relationships than the linear relationship. 
     In step S 130 , a target influence degree is determined based on the influence degree of the target friend on the interaction to be made by the target user with the target recommended information. 
     The target influence degree is an overall influence degree of at least one target friend on the interaction to be made by the target user with the target recommended information. 
     In an embodiment of the present disclosure, the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information may be summed up, to obtain the target influence degree. 
     Time when each of the target friends interacts with the target recommended information is different from one another, that is, some interactions happen earlier, while other interactions happen later. The different time of the interactions made by the target friend with the target recommended information have different influence degrees on the interaction to be made by the target user with the target recommended information. Therefore, according to an embodiment of the present disclosure, the influence degree of interactions happened earlier may be adjusted with a time attenuation factor, so that the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information matches the timing of the interaction, thereby improving the accuracy of the target influence degree determined by summing up the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information. 
     In step S 140 , a probability degree of the interaction to be made by the target user with the target recommended information is determined based on the target influence degree. 
     In an embodiment of the present disclosure, the target influence degree may be combined with a determined interest level of the target user in the target recommended information, to determine the probability degree of the interaction to be made by the target user with the target recommended information. In an embodiment of the present disclosure, the target influence degree may also solely serve as the probability degree of the interaction to be made by the target user with the target recommended information. 
     In step S 150 , the target recommended information is pushed to the target user, in a case that the probability degree meets a preset condition. 
     In an embodiment of the present disclosure, the preset condition may be determined according to actual usage demands. 
     It can be seen that, based on a discovery that the rule of interaction made by the user with the shared information published by a friend is relevant to an influence of the friend on interaction to be made by the user with recommended information, in an embodiment of the present disclosure the data of interaction made by the target user with the previously shared information published by the target friend is determined, for the target friend among the one or more friends of the target user, who has interacted with the target recommended information. Then the influence degree of the target friend on the interaction to be made by the target user with the target recommended information is determined. Then the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information is integrated to determine the target influence degree of the friend who has interacted with the target recommended information, on the interaction to be made by the target user with the target recommended information is determined. The probability degree of the interaction to be made by the target user with the target recommended information is determined based on the target influence degree, for pushing the target recommended information. Because the influence degree of the friend on the interaction to be made by the target user with the target recommended information is referred to in determining the probability degree of the interaction to be made by the target user with the target recommended information in an embodiment of the present disclosure, accuracy of the determined probability that the user interacts with the recommended information is increased, so as to improve the effectiveness of pushing the recommended information. 
     In an embodiment of the present disclosure, the data of interaction made by the target user with the previously shared information published by the target friend has the linear relationship with the influence degree of the target friend on the interaction to be made by the target user with the target recommended information. Therefore, according to an embodiment of the present disclosure, the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information may be determined based on the linear relationship and data of interaction made by the target user with the previously shared information published by the target friends. 
     The linear relationship is mainly expressed by a monadic linear regression equation. According to an embodiment of the present disclosure, the data of interaction made by the target user with the previously shared information published by the target friend, and the influence degree of the target friend on the interaction to be made by the target user with the target recommended information, serve as variables of the monadic linear regression equation. Then equation is solved by calculating a coefficient and a constant with the monadic linear regression equation. By the equation, an influence degree corresponding to interaction data can be calculated. 
     According to an embodiment of the present disclosure, the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information can be calculated by the following equation. 
     Based on an equation c ij =w·n ij +b, the influence degree of a target friend on the interaction to be made by the target user with the target recommended information is determined. 
     Denoting the target friend as j and the target user as i in the equation, c ij  is the influence degree of the target friend j on the interaction to be made by the target user i with the recommended information, n ij  is the number of interactions made by the target user i with the previously shared information published by the target friend j, w is a preset interaction weight, and b is a preset constant. 
     To solve the above equation, it may be required to determine the preset interaction weight w and the preset constant b in an embodiment of the present disclosure. Then after determining the data n ij  of interaction made by the target user with the previously shared information published by the target friend, the corresponding c ij  can be obtained. 
       FIG. 2  shows a flowchart of a method for determining the interaction weigh w and the preset constant b. Referring to  FIG. 2 , the method may include step S 200  to step S 240 . 
     In step S 200 , multiple pieces of the recommended information are pushed to a user and a friend of the user. 
     The user and the friend of the user in the step S 200  may be a user sampled for determining w and b, and the friend corresponding to the sampled user. 
     In step S 210 , the number of interactions made by the friend of the user with the multiple pieces of the recommended information is counted, and the number of interactions made by the user with the recommended information with which the friend of the user has interacted is counted. 
     For example, 10 pieces of recommended information are pushed to the user and the friend of the user. The friend of the user interacts with all the 10 pieces of recommended information. After the friend of the user interacts with the 10 pieces of the recommended information, the user interacts with only 3 of the 10 pieces of information. Then it can be determined that the number of interactions made by the friend of the user with the multiple pieces of the recommended information is 10, and the number of interactions made by the user with the recommended information with which the friend of the user has interacted is 3. 
     The number of interactions made by the user with the recommended information with which the friend of the user has interacted is the number of interactions made by the user with the recommended information under the condition that the friend of the user has interacted with the recommended information. In a case that the friend of the user did not interact with the recommended information before the user interacts with the recommended information, the interaction made by the user with the recommended information which has not been interacted with the friend of the user, should not be counted into the number of interactions made by the user with the recommended information with which the friend of the user has interacted. 
     In step  220 , a ratio of the number of interactions made by the user with the recommended information with which the friend of the user has interacted, to the number of interactions made by the friend of the user with the multiple pieces of the recommended information, is determined as a sample value c sample  of the influence degree of the friend of the user on the interaction to be made by the user with the recommended information. 
     For example, in a case that the number of interactions made by the friend of the user with the multiple pieces of the recommended information is 10 and the number of interactions made by the user with the recommended information with which the friend of the user has interacted is 3, the sample value c sample  of the influence degree of the friend of the user on the interaction to be made by the user with the recommended information is determined to be 3/10. 
     In an embodiment of the present disclosure, the user may have multiple friends. The number of interactions made by a friend of the user with the multiple pieces of the recommended information, and the number of interactions made by the user with the recommended information with which the friend of the user has interacted, may be determined for each of the friends of the user, and then the influence degree corresponding to each of the friends of the user is calculated as the sample value C sample  of the influence degree. 
     Apparently, in an embodiment of the present disclosure, the user may only have one friend. 
     In step  230 , the number n sample  of history interactions made by the user with the previously shared information published by the friend of the user is acquired. 
     The step  230  may be performed as the step S 110  shown in  FIG. 1 . 
     In step S 240 , the w and the b are determined by performing a multiple regression analysis algorithm on the sample value c sample  of the influence degree and the number of the history interactions n sample . 
     According to an embodiment of the present disclosure, the monadic linear regression equation may be established. Using the sample value C sample  of the influence degree and the number of the history interactions n sample  as variables, w and b are calculated with the multiple regression analysis algorithm. 
     After calculating w and b, the influence degree c ij  of the target friend on the interaction to be made by the target user with the target recommended information may be calculated on the basis of the data n 1  of interaction made by the target user with the previously shared information published by the target friend. 
     In this embodiment of the present disclosure, the number n ij  of interactions made by the target user i with the previously shared information published by the target friend j may be an integrated number of interactions obtained by integrating the number of interactions of each preset type made by the target user i with the previously shared information published by the target friend j, and the corresponding w may be an integrated interaction weight. Correspondingly, the number n sample  of history interactions may be an integrated number of interactions in the calculation shown in  FIG. 2 . 
     According to an embodiment of the present disclosure, the type of interaction made by the target user with the previously shared information published by the target friend may be preset. Accordingly, the number of interactions of each preset type made by the target user with the previously shared information published by the target friend is represented as an eigenvector. Each preset type corresponds to a type of interactions. The eigenvectors are collected to acquire an eigenvector set, which serves as the data of interaction made by the target user with the previously shared information published by the target friend. 
     For example, the preset interaction type includes making comments or giving the thumbs-up by the target user for the previously shared information published by the target friend, and a chatting frequency (such as an average number of chatting in each day) between the target user and the target friend. According to an embodiment of the present disclosure, for each of the target friends, the number of times of making comments and the number of times of giving the thumbs-up by the target user for the previously shared information published by the target friend, and the chatting frequency between the target user and the target friend may be obtained as eigenvectors, which are collected to acquire the eigenvector set n ij . 
     The n ij  may be expressed as n ij =(h ij ,k ij ,m ij ), where h ij  is the number of times of giving the thumbs-up by the target user i for the previously shared information published by the target friend j, k ij  is the number of times of making comments by the target user i on the previously shared information published by the target friend j, and m ij  is the chatting frequency between the target user i and the target friend j. 
     Accordingly, w is expressed as w=(w h ,w k ,w m ), where w h  is a weight of giving the thumbs-up, w k  is a weight of making comments, and w m  is a weight of chatting frequency. 
     Accordingly, in the method shown in  FIG. 2 , the number n sample  of history interactions may be the eigenvector set of the numbers of preset types of interactions, and w may be a set of weights for all the preset types. 
     The giving the thumbs-up and the making comments by the target user for the previously shared information published by the target friend, and the chatting frequency between the target user and the target friend as shown above, are only examples of the preset interaction type. The specific form of the preset interaction type may be determined based on usage, and apparently, there may be only one preset interaction type. 
       FIG. 3  shows another flowchart of an information recommendation method according to an embodiment of the present disclosure, and the method may be applied to a server or other devices. Referring to  FIG. 3 , the method may include step S 300  to step S 350 . 
     In step S 300 , at least one target friend j who has interacted with target recommended information among one or more friends of a target user i is determined. 
     In step S 310 , the number of interactions of each preset type made by the target user i with the previously shared information published by the target friend j is determined, and the numbers of interactions of each preset type are collected to acquire a set n ij . 
     In step S 320 , an influence degree of the target friend j on interaction to be made by the target user i with the target recommended information is determined according to an equation c ij =w·n ij +b, so as to acquire the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information, where c ij  is the influence degree of the target friend j on the interaction to be made by the target user i with the target recommended information, w is a preset of weights of all the preset types, and b is a preset constant. 
     In step S 330 , a target influence degree is determined based on the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information. 
     In step S 340 , a probability degree of the interaction to be made by the target user with the target recommended information is determined based on the target influence degree. 
     In step S 350 , the target recommended information is pushed to the target user, in a case that the probability degree meets a preset condition. 
     In this embodiment of the present disclosure, after the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information is obtained, the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information is integrated to acquire the target influence degree. 
     The target influence degree may be determined by determining the target influence degree according to an equation 
     
       
         
           
             
               InfluScore 
               = 
               
                 
                   ∑ 
                   
                     j 
                     ∈ 
                     N 
                   
                 
                  
                 
                   c 
                   ij 
                 
               
             
             , 
           
         
       
     
     where InfluScore is the target influence degree, and N is a set of at least one target friend who has interacted with the target recommended information among the one or more friends of the target user. That is, the influence degrees of the target friends on the interaction to be made by the target user with the target recommended information are summed up to acquire the target influence degree. 
     Alternatively, the target influence degree may be determined by determining the target influence degree according to an equation InfluScore=newc ij +f·Incluscore_old, where InfluScore is the target influence degree, newc ij  is an influence degree of the target friend, who made the latest interaction with the target recommended information, on the interaction to be made by the target user with the target recommended information, f is a current time attenuation factor, and InfluScore_old is a sum of the influence degrees of other target friends. A calculation method for InfluScore_old is the same as that for InfluScore, and is not further described here. 
     The timing of making interactions by the target friends with the target recommended information is different from one another, and different timing of the interactions leads to a difference in the influence degrees of the target friends on the interaction to be made by the target user with the target recommended information. Therefore, when a new target friend interacts with the target recommended information, the influence degree generated by other friend previously should be attenuated. That is, InfluScore=newc ij +f·Incluscore_old. Apparently, InfluScore_old is also attenuated with interaction time. 
     For example, the target friends A1, A2 and A3 are friends influencing the target user, and the target friends A1, A2 and A3 interacts with the target recommended information in sequence. When A2 interacts with the target recommended information, the target influence degree is calculated as the influence degree of A2+f2*the influence degree of A1. When A3 interacts with the target recommended information, the target influence degree is calculated as the influence degree of A3+f3*(the influence degree of A2+f2*the influence degree of A1). 
     The time attenuation factor f may be a reciprocal of the current time, for example, f2 may be a reciprocal of the time when A2 interacts with the target recommended information, which is also true for f3. 
     After determining the target influence degree, according to an embodiment of the present disclosure, the probability degree of the interaction to be made by the target user with the target recommended information may be determined by combing the target influence degree and an interest level of the target user in the target recommended information which is determined by conventional technology. 
     According to an embodiment of the present disclosure, the interest level of the target user in the target recommended information may be determined, and then the probability degree of the interaction to be made by the target user with the target recommended information is determined based on the interest level and the target influence degree. 
     In an embodiment of the present disclosure, the interest level of the target user in the target recommended information may be determined by any conventional technology. 
     The interest level and the target influence degree may be combined by taking the interest level and the target influence degree as inputs to a model, such as a logistic regression model, to calculate an output result (that is, the probability degree of the interaction to be made by the target user with the target recommended information). 
     In an embodiment of the present disclosure, the interest level and the target influence degree may be summed up. 
       FIG. 4  shows yet another flowchart of an information recommendation method according to an embodiment of the present disclosure, and the method may be applied to a server or other devices. Referring to  FIG. 4 , the method may include step S 400  to step S 450 . 
     In step S 400 , at least one target friend j who has interacted with target recommended information among one or more friends of a target user i is determined. 
     In step S 410 , the number of interactions of each preset type made by the target user i with previously shared information published by the target friend j is determined, and the numbers of interactions of each preset type are collected to acquire a set n 1 . 
     In step S 420 , an influence degree of the target friend j on interaction to be made by the target user i with the target recommended information is determined according to an equation c ij =w·n ij +b, so as to acquire the influence degree of each of the target friends on the interaction to be made by the target user with the target recommended information, where c ij  is the influence degree of the target friend j on the interaction to be made by the target user i with the target recommended information, w is a set of weights of all the preset types, and b is a preset constant. 
     In step S 430 , a target influence degree is determined, according to an equation InfluScore=newc ij +f·Incluscore_old, where InfluScore is the target influence degree, newc ij  is an influence degree of the target friend who made the latest interaction with the target recommended information, on the interaction to be made by the target user with the target recommended information, f is a current time attenuation factor, InfluScore_old is a sum of the influence degrees of other target users than newc ij . A calculation method for InfluScore_old is the same as that for InfluScore, which is not described here. 
     In step S 440 , an interest level of the target user in the target recommended information is determined, and a probability degree of the interaction to be made by the target user with the target recommended information is determined based on the interest level and the target influence degree. 
     In step S 450 , the target recommended information is pushed to the target user, in a case that the probability degree meets a preset condition. 
     In an embodiment of the present disclosure, after the probability degree of the interaction to be made by the target user with the target recommended information is obtained, it may be determined whether the probability degree is larger than a preset probability degree. In a case that the probability degree is larger than the preset probability degree, it is determined that the probability meets the preset condition, and then the target recommended information is pushed to the target user. 
     The target recommended information may be one of multiple pieces of candidate recommended information. According to an embodiment of the present disclosure, the probability degree of the interaction to be made by the target user with each of the candidate recommended information may be determined in the above manner of determining the probability degree, thereby ranking the candidate recommended information based on their probability degree after determining the probability degree of the interaction to be made by the target user with each of the candidate recommended information. In a case that a rank of the target recommended information meets a preset rank condition, it is determined that the probability degree meets the preset condition, and then the target recommended information is pushed to the target user. In a case that a rank of the target recommended information is in a preset range of rank, it can be determined that the probability degree meets the preset condition, and the target recommended information can be pushed to the target user. 
     An application of the information recommendation method according to an embodiment of the present disclosure is to push advertisements, which is taken as an example to explain an application of the information recommendation method according to an embodiment of the present disclosure. 
       FIG. 5  shows relationships in a circle of friends, where the circle of friends is a friend social circle provided by a social application. Referring to  FIG. 5 , it is assumed that the friends of the target user i are j 1 , j 2 , j 3 , j 4  and j 5 , and j 1 , j 2  and j 3  interact with the advertisement respectively at time t 1 , t 2  and t 3 , and t 1 &lt;t 2 &lt;t 3 ; j 4  does not interact with the advertisement although the advertisement can be viewed by j 4 , and j 5  cannot view the advertisement. Therefore, only the friends j 1 , j 2  and j 3  influence the user i, and it is determined that the target friends who have interacted with the advertisement among the one or more friends of the target user i are j 1 , j 2  and j 3 . 
     For the friend j 1 , the number of interactions of each preset type made by the user i with the previously shared information published by the friend j 1  in a preset time period may be determined, and the set of the numbers of interactions of all the preset types serves as the number n ij1  of interactions made by the user i with the previously shared information published by the friend j 1 . The preset type may include giving the thumbs-up or making comments by the user i for the previously shared information published by the friend j 1 , and the chatting frequency between the user i and the friend j 1 , which apparently may also be customized otherwise. 
     For the friend j 2 , the number of interactions of each preset type made by the user i with the previously shared information published by the friend j 2  in a preset time period can be determined, and the set of the numbers of interactions of all the preset types serves as the number n ij2  of interactions made by the user i with the previously shared information published by the friend j 2 . 
     For the friend j 3 , the number of interactions of each preset type made by the user i with the previously shared information published by the friend j 3  in a preset time period can be determined, and the set of the numbers of interactions of all the preset types serves as the number n ij3  of interactions made by the user i with the previously shared information published by the friend j 3 . 
     For the friend the influence degree of the friend j 1  on the interaction to be made by the user i with the advertisement is determined according to an equation c ij =w·n ij1 +b. For the friend j 2 , the influence degree of the friend j 2  on the interaction to be made by the user i with the advertisement is determined according to an equation c ij =w·n ij2 +b. For the friend j 3 , the influence degree of the friend j 3  on the interaction to be made by the user i with the advertisement is determined according to an equation c ij =w·n ij3 +b; where w is a set of pre-calculated weights for all the preset types, and b is a pre-calculated constant. 
     After c ij1 , c ij2  and c ij3  are obtained, because the timing of the interactions between the friends j 1 , j 2  and j 3  and the advertisement is t 1 , t 2  and t 3  respectively, and t 1 &lt;t 2 &lt;t 3 , by taking attenuation of the influence degree with time into account, the target influence degree may be calculated as:  c     ij   f3+f3( c     ij2   +f2 c     ij1   , where f2 corresponds to t 2  and may be a reciprocal of t 2 , and f3 corresponds to t 3  and may be a reciprocal of t 3 . 
     After the target influence degree is obtained, the target influence degree and the interest level of the user i in the advertisement may be combined to determine the probability degree of the interaction to be made by the user i with the advertisement, so as to determine the rank of the advertisement among candidate advertisements on the basis of the probability degree of the interaction to be made by the user i with the advertisement. In a case that the determined rank is in a preset range of rank, the advertisement is pushed to the user i. After the advertisement is pushed to the user i, because the probability that the user i interacts with the advertisement is high, an interaction effect of pushing the advertisement is increased, so that the effectiveness of pushing the advertisement is improved. 
     With an information recommendation method according to an embodiment of the present disclosure, the accuracy of determined probability degree of the interaction to be made by the user with the recommended information is increased, so that the effectiveness of pushing the recommended information is improved. 
     Hereinafter an information recommendation apparatus according to an embodiment of the present disclosure is described. The information recommendation apparatus described hereinafter and the information recommendation method may be referred to each other. 
       FIG. 6  is a block diagram of an information recommendation apparatus according to an embodiment of the present disclosure, and the apparatus may be applied to a server. Referring to  FIG. 6 , and the information recommendation apparatus may include a target friend determining module  100 , an interaction data determining module  200 , an influence degree determining module  300 , a target influence degree determining module  400 , a probability degree determining module  500 , and a recommendation module  600 . 
     The target friend determining module  100  is configured to determine a target friend who has interacted with target recommended information among one or more friends of a target user. 
     The interaction data determining module  200  is configured to determine data of interaction made by the target user with previously shared information published by the target friend. 
     The influence degree determining module  300  is configured to determine an influence degree of the target friend on interaction to be made by the target user with the target recommended information based on the data of interaction made by the target user with the previously shared information published by the target friend. 
     The target influence degree determining module  400  is configured to determine a target influence degree based on the influence degree of the target friend on the interaction to be made by the target user with the target recommended information. 
     The probability degree determining module  500  is configured to determine a probability degree of the interaction to be made by the target user with the target recommended information based on the target influence degree. 
     The recommendation module  600  is configured to push the target recommended information to the target user, in a case that the probability degree meets a preset condition. 
     The data of interaction made by the target user with the previously shared information published by the target friend is in a linear relationship with the influence degree of the target friend on the interaction to be made by the target user with the target recommended information.  FIG. 7  shows an optional structure of the influence degree determining module  300 . Referring to  FIG. 7 , the influence degree determining module  300  may include a linear calculation unit  310 . 
     The linear calculation unit  310  is configured to determine the influence degree of the target friend on the interaction to be made by the target user with the target recommended information based on the linear relationship and the data of interaction made by the target user with the previously shared information published by the target friends. 
       FIG. 8  shows an optional structure of the linear calculation unit  310 . Referring to  FIG. 8 , the linear calculation unit  310  may include an equation calculation unit  311 . 
     The equation calculation unit  311  is configured to determine an influence degree of one target friend on the interaction to be made by the target user with the target recommended information, according to an equation c ij =w·n ij +b, where c ij  is the influence degree of the target friend j on the interaction to be made by the target user i with the target recommended information, n ij  is the number of interactions made by the target user i with the previously shared information published by the target friend j, w is a preset interaction weight, and b is a preset constant. 
       FIG. 9  shows another block diagram of an information recommendation apparatus according to an embodiment of the present disclosure. As shown in conjunction with  FIG. 6 ,  FIG. 8  and  FIG. 9 , the apparatus may further include a parameter calculation module  700 . 
     The parameter calculation module  700  is configured to push multiple pieces of the recommended information to a user and a friend of the user; count the number of interactions made by the friend of the user with the multiple pieces of the recommended information, and the number of interactions made by the user with the recommended information with which the friend of the user has interacted; determine a ratio of the number of interactions made by the user with the recommended information with which the friend of the user has interacted, to the number of interactions made by the friend of the user with the multiple pieces of the recommended information, as a sample value C sample  of the influence degree of the friend of the user on the interaction to be made by the user with the recommended information; acquire the number n sample  of history interactions made by the user with the previously shared information published by the friend of the user; and determine the w and the b by performing a multiple regression analysis algorithm on the sample value C sample  of the influence degree and the number n sample  of history interactions. 
     The n ij  includes a set of the numbers of interactions of all the preset types made by the target user i with the previously shared information published by the target friend j. Accordingly, the w includes a set of the weights of all the preset types. 
       FIG. 10  shows an optional structure of the target influence degree determining module  400 . Referring to  FIG. 10 , the target influence degree determining module  400  may include an addition processing unit  410 . 
     The addition processing unit  410  is configured to determine the target influence degree according to an equation 
     
       
         
           
             
               InfluScore 
               = 
               
                 
                   ∑ 
                   
                     j 
                     ∈ 
                     N 
                   
                 
                  
                 
                   c 
                   ij 
                 
               
             
             , 
           
         
       
     
     where InfluScore is the target influence degree, and N is a set of at least one target friend who has interacted with the target recommended information among the one or more friends of the target user. 
       FIG. 11  shows another optional structure of the influence degree determining module  400  according to an embodiment of the present disclosure. Referring to  FIG. 11 , the target influence degree determining module  400  may include an attenuation and addition processing unit  420 . 
     The attenuation and addition processing unit  420  is configured to determine the target influence degree according to an equation InfluScore=newc ij +f·Incluscore_old, where InfluScore is the target influence degree, newc ij  is an influence degree of the target friend, who has interacted with the target recommended information in a time period just before the current time, on the interaction to be made by the target user with the target recommended information, f is a current time attenuation factor, InfluScore_old is a sum of the influence degrees of other target users than newc ij , and a calculation method for InfluScore_old is the same as that for InfluScore, which is not described here. 
     The probability degree determining module  500  may be configured to determine an interest level of the target user in the target recommended information, and determine a probability degree of the interaction to be made by the target user with the target recommended information based on the interest level and the target influence degree. 
     In an aspect, the recommendation module  600  may be configured to determine that the probability degree meets the preset condition and push the target recommended information to the target user, in a case that the probability degree is greater than a preset probability degree. 
     In another aspect, the recommendation module  600  may be configured to rank candidate recommended information including the target recommended information according to the probability degree of each candidate recommended information, after determining the probability degree of interaction to be made by the target user with each candidate recommended information, and determine that the probability degree meets the preset condition and push the target recommended information to the target user, in a case that a rank of the target recommended information meets a preset rank condition. 
     With the information recommendation apparatus according to an embodiment of the present disclosure, the accuracy of determined probability degree of the interaction to be made by the user with the recommended information is increased, so that the effectiveness of pushing the recommended information is improved. 
     Optionally,  FIG. 12  shows a hardware block diagram of another information recommendation apparatus according to an embodiment of the present disclosure. Referring to  FIG. 12 , the apparatus may include: a processor  1 , a communication interface  2 , a memory  3 , and a communication bus  4 . 
     The processor  1 , the communication interface  2 , and the memory  3  communicate with each other via the communication bus  4 . 
     Optionally, the communication interface  2  may be an interface of a communications module, such as an interface of a GSM module. 
     The processor  1  is configured to execute a program. 
     The memory  3  is configured to store the program. 
     The program may include a program code, where the program code includes an operation instruction of computer. 
     The processor  1  may be a central processor unit CPU, an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present disclosure. 
     The memory  3  may include a high speed RAM memory, and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. 
     The program may be specifically configured to: 
     determine a target friend who has interacted with target recommended information among one or more friends of a target user; 
     determine data of interaction made by the target user with previously shared information published by the target friend; 
     determine an influence degree of the target friend on interaction to be made by the target user with the target recommended information based on the data of interaction made by the target user with the previously shared information published by the target friend; 
     determine a target influence degree based on the influence degree of the target friend on the interaction to be made by the target user with the target recommended information; 
     determine a probability degree of the interaction to be made by the target user with the target recommended information based on the target influence degree; and 
     push the target recommended information to the target user, in a case that the probability degree meets a preset condition. 
     A server is further provided to an embodiment of the present disclosure, where the server may include the information recommendation apparatus described above. 
     The embodiments of the present disclosure are described in a progressive manner, and each embodiment places emphasis on the difference from other embodiments. Therefore, the embodiments may be referred to one another for the same or similar parts. Since the apparatus embodiments correspond to the method embodiment, the description of the apparatus embodiments is simple. For the relevant portions, one may refer to the description of the method parts. 
     As further be appreciated by those skilled in the art, the units and algorithmic steps in the examples described according to the embodiments disclosed herein can be implemented in forms of an electronic hardware, computer software or the combination thereof. To illustrate the interchangeability of the hardware and the software clearly, the components and the steps in the examples are described generally according to functions in the above description. Whether hardware or software is used to implement the functions depending on a specific application and design constraints for the technical solution. For each specific application, different methods may be used by those skilled in the art to implement the described function, and such implementation should not be considered as departing from the scope of the disclosure. 
     The steps of the method or algorithm described according to the embodiments disclosed herein may be implemented in forms of hardware, a software module executed by a processor or the combination thereof. The software module may be stored in a Random Access Memory (RAM), a memory, a Read-Only Memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hardware disk, a movable magnetic disk, CD-ROM or any other forms of storage medium well known in the art. 
     The above description of the embodiments disclosed herein enables those skilled in the art to implement or use the present disclosure. Numerous modifications to the embodiments will be apparent to those skilled in the art, and the general principle herein can be implemented in other embodiments without deviation from the spirit or scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments described herein, but in accordance with the widest scope consistent with the principle and novel features disclosed herein.