CN112330362A - Rapid data intelligent analysis method for internet mall user behavior habits - Google Patents
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Abstract
The invention relates to the technical field of network marketing and artificial intelligence, in particular to a rapid data intelligent analysis method for internet mall user behavior habits. According to the current behavior of the user, more targeted sales recommendation is given; by utilizing the advantages of multiple cells contained in a microcolumn in the HTM, the input position information can be distinguished, and the input active cell set is reset into a learning cell set, so that the HTM can learn aiming at the current input sequence when learning online, and the learning efficiency is improved; in the process of learning the repetitive sequence, the number of active cell sets is reduced, the number of cells associated with the cell sets can be effectively reduced, the possibility of occurrence of cycle prediction is reduced, and the learning effect of the HTM is improved; the invention sets the synapse value in the newly increased dendritic branches above the connectivity threshold, and improves the learning efficiency of the HTM.
Description
Technical Field
The invention relates to the technical field of network marketing and artificial intelligence, in particular to a rapid data intelligent analysis method for user behavior habits of an internet mall.
Background
The behavior habits of the users of the internet shopping mall are all carried out in specific scenes, and the users also know the products through the scenes, so that the users have different requirements in different scenes. If the product selling points are connected with the requirements of the user, the pain points of the user are effectively triggered by utilizing the scene, the emotional resonance of the consumer is caused, the purchasing desire is stimulated, and a good interaction relation can be established, so that the viscosity and loyalty of the consumer are formed. For the initial user or the user with less consumption information, the consumption habit of the user is difficult to dig out due to insufficient user information, and further, the user is difficult to provide targeted recommendation and the commodity transaction rate is improved. And the scene marketing can construct the display sequence of the commodities according to the previous and subsequent causal relationships or functions, and further give more targeted recommendations according to the current selection of the user, so how to rapidly construct the memory of the scene, and the memory is used as the basis of the subsequent user behavior habit statistics to become the basis of rapid intelligent analysis.
Brain-like learning is a hotspot of current research in the fields of artificial intelligence and machine learning. Hierarchical Temporal memory (htm) is a machine learning technique that mimics the processing mechanism of information by the human brain by simulating the organization and organization of cortical cells. HTM is essentially a memory-based system. The HTM network is trained by a large amount of time-dependent data, a large number of pattern sequences are stored, and the next possible input is predicted through the memorized pattern sequences.
Unlike the existing artificial neural network, the HTM takes cells as a basic unit and is managed in a hierarchical manner; several cells are firstly combined into a micro-column, and then the micro-column forms HTM network space. The spatial pool algorithm and the temporal pool algorithm are two important steps in training the HTM, and the spatial pool algorithm is first used to select some of the activated microcolumns from all the microcolumns to correspond to the current input. Selecting partially activated cells from the microcolumns by using a time pool algorithm to express and input the position information, and establishing association between input and output by adjusting relevant dendritic branches on the active cells to learn; and simultaneously using the active cells and the constructed dendritic branches to predict the input at the next moment.
The current time pool algorithm only uses a simple Hebbian rule, establishes the association between active cells at two adjacent moments before and after by adjusting the connection value of synapses in dendritic branches, and learns the characteristics of sequences; and the sequence learning task can be completed only after the connected synapses in the dendritic branches are accumulated to a certain threshold value. If a fast learning model is to be constructed, a novel time pool algorithm must be designed, and the efficiency and effect of the HTM learning sequence are improved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to solve the problems in the background art, an improved method for analyzing the user behavior habits of the internet mall quickly and intelligently by data is provided, so that the problems of low learning efficiency and poor learning effect during the htm (historical Temporal memory) quick training are solved.
The technical scheme adopted by the invention for solving the technical problems is as follows: a rapid data intelligent analysis method for Internet mall user behavior habits comprises an Internet mall user behavior pattern sequence, a coding module, a space pool module and a time module, wherein the time module comprises a microcolumn set for acquiring input activation, a learning cell set, a dendritic branch regulation, an active cell set regulation and a prediction unit, and the method comprises the following steps:
step 1, constructing a user behavior sequence aiming at a scene of an internet mall, and forming different user behavior combination time sequence modes;
step 2, aiming at different internet mall marketing scenes, taking a user behavior combination mode with a time sequence characteristic as a training object of an HTM rapid training model;
step 3, selecting a part of microcolumns from all microcolumns by using a space pool algorithm to activate, and enabling the activated microcolumns to correspond to a certain commodity in the current user behavior mode;
step 4, generating a learning cell set and a temporary active cell set on the activated microcolumn by using the input position information, so that the learning process can be performed according to the sequence of the current position, the learning accuracy of the HTM is improved, the number of cells associated with the method is effectively reduced in the process of learning a repeated sequence, the possibility of occurrence of cyclic prediction is reduced, and the learning effect of the HTM is improved;
step 5, adjusting related and adjacent input dendritic branches on the learning cells, and setting a synapse value in the newly-added dendritic branches as a communication value according to the characteristics of online learning, so that the time pool algorithm can quickly form memory and learning of a sequence through one-time training, and the learning efficiency of the HTM is improved;
and 6, predicting the commodity at the next moment by using the adjusted active cell set, and setting the commodity as a recommended commodity.
Furthermore, the space pool module is used for acquiring the activated microcolumn set, the time pool module comprises a learning cell set generating unit, an input end of the learning cell set generating unit is connected with an output end of the space pool module, and an output end of the learning cell set generating unit is sequentially connected with the dendritic branch adjusting unit and the predicting unit.
The invention has the beneficial effects that:
1. the method comprises the steps that a correlation mode of user behaviors is built according to scenes of an internet mall, and more targeted sales recommendations can be given according to current behaviors of users for users who cannot acquire historical information;
2. when the learning cells are generated, the input position information can be distinguished by utilizing the advantages of a plurality of cells contained in the microcolumn in the HTM, the input active cell set is reset to be the learning cell set, and the input current position information is expressed in the prediction process, so that the association between the subsequent learning content and the input of the current position is conveniently established, the HTM can learn aiming at the current input sequence in the online learning process, and the learning efficiency is improved; in the process of learning the repetitive sequence, the number of active cell sets is reduced, the number of cells associated with the cell sets can be effectively reduced, the possibility of occurrence of cycle prediction is reduced, and the learning effect of the HTM is improved;
3. according to the method, only the related adjacent input dendritic branches are adjusted, and the synapse value in the newly-added dendritic branches is set above the connection threshold aiming at the characteristic of quick training, so that the algorithm can form memory and learning of a mode sequence through one-time training, and the learning efficiency of the HTM is improved.
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The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart of the analytical method of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
Example 1:
the rapid data intelligent analysis method for the internet mall user behavior habits, shown in fig. 1, comprises an internet mall user behavior pattern sequence, an encoding module, a space pool module and a time module, wherein the time module comprises six steps of obtaining an input activated microcolumn set, generating a learning cell set, adjusting dendritic branches, adjusting an active cell set and predicting:
step 1, constructing commodity sequences by using scenes to form different commodity combination time sequence modes.
Step 2, aiming at different marketing scenes, taking a user behavior combination mode with a time sequence characteristic as a training object of the HTM rapid training model;
step 3, acquiring a microcolumn set activated by input, wherein the microcolumn set can be generated by a space pool algorithm, selecting a part of microcolumns from all the microcolumns for activation, and enabling the activated microcolumns to correspond to the current input;
step 4, generating a prediction cell set of which the learning cell set depends on the previous moment, and selecting learning cells capable of expressing input current position information for the unpredicted activated microcolumns so that the learning process is more specific to the sequence at the current position; in particular, the amount of the solvent to be used,
step 4.1, the method for generating the learning cell set comprises the following steps:
step 41.1, if the cell on the activated microcolumn is predicted by the input at the previous time, the cell is set as the currently input learning cell, and is expressed as:
step 4.1.2, if all the cells on the activated microcolumn are not predicted by the input at the previous moment, randomly selecting the cell with the least number of dendritic branches in all the cells on the microcolumn as the learning cell, and expressing that:
the learning cells selected by the method form a learning cell set; wherein the content of the first and second substances,
the ith cell on the jth microcolumn at time t is selected as a learning cell, i is the number of the cell on the microcolumn, j is the number of the microcolumn, WtInputting the activated microcolumn set for the time t,
is a predicted cell matrix at the time t-1; minj(the segment′s number of celli，j) Numbering the cells on the jth microcolumn that contain the fewest dendritic branches;
and 4.2, generating active cells in the activated microcolumns screened by the space pool algorithm by using the following rule, wherein if the activated microcolumns have predicted cells, the cells are set as the active cells, and if the activated microcolumns do not have the predicted cells, all the cells on the microcolumns are set as the active cells.
Constructing a temporary active cell set by the active cells selected by the method; wherein
Indicating that the ith cell on the jth microcolumn at the time t is set as an active cell, i is the number of the cell on the microcolumn, j is the number of the microcolumn, WtInputting the activated microcolumn set for the time t,
indicating that the ith cell on the jth microcolumn is a predicted cell at the t-1 moment;
the method comprises the following steps of setting non-learning cells in an active cell set to be in an inactive state, and accurately expressing input current position information, wherein the main operations are as follows:
wherein A istRepresenting the active cell matrix generated by the input at time t,
inputting a corresponding learning cell matrix at a representative time t;
step 5, in the step of adjusting the dendritic branches, the dendritic branches needing to be adjusted are either active or newly added on learning cells, the association between the front input and the rear input is established, and synapse values in the newly added dendritic branches are set to be values of a communication threshold value and above, so that the learning efficiency is improved; specifically, the process of adjusting dendritic branches in step 3 is as follows:
step 5.1, if the learning cell currently processed is the cell predicted by the input of the previous moment, adjusting the active dendritic branch on the cell to strengthen the association with the input of the previous moment, wherein the selection condition of the active dendritic branch on the cell is as follows:
step 5.2, if the learning cell processed currently is not the cell predicted by the last input, adding a dendritic branch on the cell, constructing synapse with the learning cell input at the last time, and forming an association with the last input, wherein the added dendritic branch is represented as:
wherein the content of the first and second substances,
the connectivity matrix of the kth dendritic branch on the ith cell in the jth microcolumn, activeThreshold is the activity threshold of the dendritic branch,
is a dendritic branch newly added on the ith cell in the jth microcolumn, CONNECTED _ PERMANANCE is the threshold of connectivity of synapses in the dendritic branch;
and inputting a corresponding learning cell matrix for the t-1 moment.
Step 6, adjusting the active cell set and predicting, and predicting through the active cells with a reduced range, so that the distinguishing capability of the learning cells generated by subsequent input on the position information can be improved, and meanwhile, the association between the subsequent learning content and the input of the current position is conveniently established; the algorithm improves the learning effect and efficiency of the HTM on the sequence aiming at the characteristic of quick training.
Based on the method, the invention also provides a rapid data intelligent analysis method for analyzing the behavior habit path of the user in the Internet mall, which comprises a space pool module and a time pool module, wherein the space pool module is used for acquiring the activated microcolumn set; the time pool module comprises a learning cell set generation unit, the input end of the learning cell set generation unit is connected with the output end of the space pool module, and the output end of the learning cell set generation unit is sequentially connected with the dendritic branch regulation unit and the prediction unit.
Example 2:
in this embodiment, taking "abab" as an example of the input sequence during the fast training of the present invention, first, through the learning of the space pool, it is assumed that the input a activates the 1 and 3 micro-columns, the input b activates the 2 and 4 micro-columns, and each micro-column has 4 cells, and the threshold value of the connectivity of the dendrites is set to 0.8. The following describes the process when learning online:
for the first input a in the sequence, since there is no context, the learning cells that assume the time pool to produce this input are: cell1，1And cell3，1The first cell of the No. 1 microcolumn and the first cell of the No. 3 microcolumn, respectively, and no dendritic branches need to be adjusted, while the active cells are also reset to cells1，1And cell3，1。
For the second input b in the sequence, the active cells at the previous time are not predicted to be the current input, so there are no predicted cells on the activated microcolumn, assuming that the time pool produces the learning cells of this input: cell2，1And cell4，1The first cell of the No. 2 microcolumn and the first cell of the No. 4 microcolumn, respectively, have a new dendritic branch on both cells, respectively [ cell1，1＝0.8，cell3，1＝0.8]Indicating that both cells establish synaptic connections to the cell1，1And cell3，1And the synapse is in a connected state. While also resetting the active cells to cells2，1And Cell4，1。
For the third input a in the sequence, the active cell at the previous time is not predicted to be the current input, so there are no predicted cells on the activated microcolumn, assuming that the time pool produces the learning cells of this input: cell1，2And cell3，2The second cell of the No. 1 microcolumn and the second cell of the No. 3 microcolumn, respectively, have a new dendritic branch on both cells, respectively [ cell2，1＝0.8，cell4，1＝0.8]Indicating that both cells establish synaptic connections to the cell2，1And cell4，1And the synapse is in a connected state. While also resetting the active cells to cells1，2And cell3，2。
For the fourth input b in the sequence, the active cells at the previous time are not predicted to be the current input, so there are no predicted cells on the activated microcolumn, assuming that the time pool produces the learning cells of this input: cell2，2And cell4，2The second cell of the No. 2 microcolumn and the second cell of the No. 4 microcolumn, respectively, are newly added with dendritic branches, respectively, [ cell ]1，2＝0.8，cell3，2＝0.8]Indicating that both cells establish synapses to cells1，2And cell3，2And the synapse is in a connected state. While also resetting the active cells to cells2，2And cell4，2。
Through fast training, the HTM can learn the complete content of the "abab" sequence, but the existing time-pool learning algorithm can only learn the "aba" sequence, and the learning of the fourth step only strengthens the association between the first input and the second input.
The method comprises the steps that a correlation mode of user behaviors is built according to scenes of an internet mall, and more targeted sales recommendations can be given according to current behaviors of users for users who cannot acquire historical information; when the learning cells are generated, the input position information can be distinguished by utilizing the advantages of a plurality of cells contained in the microcolumn in the HTM, the input active cell set is reset to be the learning cell set, and the input current position information is expressed in the prediction process, so that the association between the subsequent learning content and the input of the current position is conveniently established, the HTM can learn aiming at the current input sequence in the online learning process, and the learning efficiency is improved; in the process of learning the repetitive sequence, the number of active cell sets is reduced, the number of cells associated with the cell sets can be effectively reduced, the possibility of occurrence of cycle prediction is reduced, and the learning effect of the HTM is improved; according to the method, only the related adjacent input dendritic branches are adjusted, and the synapse value in the newly-added dendritic branches is set above the connection threshold aiming at the characteristic of quick training, so that the algorithm can form memory and learning of a mode sequence through one-time training, and the learning efficiency of the HTM is improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (2)
1. A quick data intelligent analysis method for Internet mall user behavior habits comprises an Internet mall user behavior mode sequence, a coding module, a space pool module and a time module, and is characterized in that: the time module comprises a microcolumn set for acquiring input activation, a learning cell set, a dendritic branch regulation unit, an active cell set regulation unit and a prediction unit, and comprises the following steps:
step 1, constructing a user behavior sequence aiming at a scene of an internet mall, and forming different user behavior combination time sequence modes;
step 2, aiming at different internet mall marketing scenes, taking a user behavior combination mode with a time sequence characteristic as a training object of an HTM rapid training model;
step 3, selecting a part of microcolumns from all microcolumns by using a space pool algorithm to activate, and enabling the activated microcolumns to correspond to a certain commodity in the current user behavior mode;
step 4, generating a learning cell set and a temporary active cell set on the activated microcolumn by using the input position information, so that the learning process can be performed according to the sequence of the current position, the learning accuracy of the HTM is improved, the number of cells associated with the method is effectively reduced in the process of learning a repeated sequence, the possibility of occurrence of cyclic prediction is reduced, and the learning effect of the HTM is improved;
step 5, adjusting related and adjacent input dendritic branches on the learning cells, and setting a synapse value in the newly-added dendritic branches as a communication value according to the characteristics of online learning, so that the time pool algorithm can quickly form memory and learning of a sequence through one-time training, and the learning efficiency of the HTM is improved;
and 6, predicting the commodity at the next moment by using the adjusted active cell set, and setting the commodity as a recommended commodity.
2. The method for rapid intelligent analysis of data of internet mall user behavior habits according to claim 1, wherein: the space pool module is used for obtaining an activated microcolumn set, the time pool module comprises a learning cell set generating unit, the input end of the learning cell set generating unit is connected with the output end of the space pool module, and the output end of the learning cell set generating unit is sequentially connected with the dendritic branch adjusting unit and the predicting unit.
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