In the large data analysis and the application of complex network transmission, the recommendation technology shows the problem of low efficiency and high error. Because of the dynamic similarity of different users, it is recommended to reduce the accuracy by calculating the different user’s needs. The similarity of the dynamic changes will lead to that the neighbor and the user demand is not consistent.
When the user needs have multiple target features, the mapping between the user similarity and the recommended content has a large deviation. Based on the mapping relationship matrix, the predicted project content leads to a poor prediction error, as shown in the formula (1).
$$ \left\{\begin{array}{l}{R}_{\mathrm{A}}=\frac{R_{\mathrm{T}}}{ \max {E}_{\mathrm{R}}{f}_{\mathrm{R}}}\\ {}{M}_{\mathrm{R}\hbox{} \mathrm{O}}=\left[\begin{array}{cc}\hfill {\displaystyle \sum_{i=1}^k{\delta_{\mathrm{S}}}^k{R}^k}\hfill & \hfill {\overline{\delta}}_{\mathrm{S}}{R}_{\mathrm{A}}\hfill \\ {}\hfill {\overline{\delta}}_{\mathrm{S}}{R}_{\mathrm{A}}\hfill & \hfill {\displaystyle \sum_{i=k+1}^n{\delta}_{\mathrm{S}}{R}^k}\hfill \end{array}\right]\\ {}{E}_{\mathrm{F}}=\frac{{\displaystyle \sum_{i=1}^n{\delta_{\mathrm{S}}}^k{R}^k}}{R\cdot {R}_{\mathrm{A}}}\end{array}\right. $$
(1)
Here, R
_{A} indicates the accuracy of recommendation. M
_{RO} represents the mapping between content and user requirements, which could satisfy the users’ needs according to the content effectively. δ
_{
S
} is the similarity weights. E
_{F} indicates the prediction error. k represents the recommended relationship between the first set of user neighbors. n represents the number of users. R represents a collection of recommended content.
From formula (1), we found that the value of K determines the mapping between the matrix of the rank and the elements. k also determines the mapping between the best subset of the search neighbors and other users. When both k and n are increased, the M
_{RO} will show the oncoordination and inconsistency.
To sum up, we will make the user needs and different users become the target. By setting up a multiobjective matrix, the coadjustment between the target and the target is carried out. The purpose of the adjustment is to weaken the differences between goals and strengthen the consistency of the target.
The nearest neighbor was chosen to ignore the difference between the user’s needs. The multiple objective matrix would be queried when predicting the recommended results. The main basis for choosing the nearest neighbor is the consistency and the similarity of the target. The recommended content for multiobject coordination is transparent. The transparency as shown in formula (2).
$$ \left\{\begin{array}{l}P=\left[\begin{array}{cc}\hfill {R}_1\cdot {\varphi}_1\hfill & \hfill {R}_m\cdot {\varphi}_m\hfill \\ {}\hfill {R}_t\cdot {\varphi}_t\hfill & \hfill {R}_{nm}\cdot {\varphi}_{nm}\hfill \end{array}\right]\\ {}{T}_R= \log \left(\frac{\left\Vert P\right\Vert }{{\displaystyle \sum_{k=1}{R}^k}}\right)\end{array}\right. $$
(2)
Here, φ is the transparency coefficient. The transparency of multiobject coordination is obtained through the calculation of the matrix P paradigm.
Transparent processing can predict the content of the project. The prediction is not related to the recommendation and mapping. Multiobjective cooperative process is as follows:

(1)
The prediction of the target user needs is equivalent to a linear similar item set.

(2)
Calculate the similarity of multiple targets. The nearest neighbor set of multiple targets would be reorganized with similarity.

(3)
Search the nearest neighbor of a target in a particular item category.

(4)
The data of these nearest neighbors would be sparse processed. Coordinate the nearest neighbor target structure, based on the similarity and reliability search for multiobjective optimization objectives.
Through the above process, the similarity and reliability of the combination of the best optimization objectives OP (T, R_{LI}) are as shown in the formula (3).
$$ \mathrm{OP}\left(T,{R}_{\mathrm{LI}}\right)=\frac{{\displaystyle \sum_{i=1}^m{R_{\mathrm{LI}}}^i{T_{\mathrm{R}}}^i}}{\delta \sqrt{{\displaystyle \sum_{i=1}^n{R_{\mathrm{LI}}}^i}}+\varphi \sqrt{{\displaystyle \sum_{i=1}^n{T_{\mathrm{R}}}^i}}} $$
(3)
The objective of the optimization is to be a linear result of the multiobjective evaluation. However, there is a certain degree of coupling after multiple target filtering. Coupling between objects can increase the diversity of users’ requirements and the accuracy of the recommendation. This kind of interference will reduce the transparency. Therefore, we will construct the embedded filter according to the similarity and preference of the multiple targets. The method of formula (4) could reduce the coupling degree between multiple targets N
_{C}. Target similarity between target user and neighbor user would be updated. The reliability of multiple targets is in the nearest neighbor combination. The minimum similarity content would be found by traversal search.
$$ {N}_{\mathrm{C}}=\mathrm{OP}\left(T,{R}_{\mathrm{LI}}\right)\frac{{\displaystyle \sum_{i=1}^m OP\left({T}_i,{R_{\mathrm{LI}}}^i\right)}}{\delta \varphi \sqrt{{\displaystyle \sum_{i=1}^n{T_{\mathrm{R}}}^i{R_{\mathrm{LI}}}^i}}} $$
(4)
Then, the multiobjective near coupling is embedded into the multiobject similarity matrix of all users. From the low coupling objective, the traversal of the nearest neighbor set select the recommended content and, finally, the recommended content filtering. The similarity and reliability measures are given to the multiobjective matrix, and the nearest neighbor set is updated in real time. Through the multiobjective coordination and embedded recommendation, the best forecast goal is pushed to the upper level service. The recommendation system model includes the user group, multigoal conversion module, comparability and reliability measure module, feedback module, nearly coupling degree measure module, recommended question processing module, and database module, as shown in Fig. 1.