CN108665323B - Integration method for financial product recommendation system - Google Patents

Integration method for financial product recommendation system Download PDF

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CN108665323B
CN108665323B CN201810484714.2A CN201810484714A CN108665323B CN 108665323 B CN108665323 B CN 108665323B CN 201810484714 A CN201810484714 A CN 201810484714A CN 108665323 B CN108665323 B CN 108665323B
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李建强
李倩
张丝雨
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SHANGHAI DIGITAL CHINA INFORMATION TECHNOLOGY SERVICE Co.,Ltd.
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Abstract

The invention discloses an integration method for a financial product recommendation system, which can fill sparse data based on a collaborative filtering algorithm of data smoothing and reduce the problem of data sparsity. The recommendation algorithm based on the demographics does not need historical data and does not depend on the attribute of an article, so that the cold start problem of a user can be solved; the two algorithms are integrated with a recommendation algorithm which has good performance and is based on item clustering and matrix decomposition, so that the use scene of the recommendation algorithm is enlarged, and the adaptability of the recommendation algorithm is improved. The integration method can effectively reduce the sparsity of data, solve the problem of cold start and improve the recommendation performance of each user.

Description

Integration method for financial product recommendation system
Technical Field
The invention belongs to the technical field of online product recommendation, and particularly relates to an integration method for a financial product recommendation system.
Background
The traditional recommendation method mostly calculates interest preference and resource similarity of users according to user score data, has low recommendation quality for sparse data and new users, and cannot maximally mine information carried by implicit data.
In recent years, aiming at the problems brought by data sparsity, in order to improve the recommendation effect, students introduce algorithms such as principal component analysis, cluster analysis, singular value decomposition and the like into a traditional collaborative filtering recommendation algorithm, and reduce the range of searching nearest neighbors by a target user through dimension reduction, so that the recommendation precision and real-time performance are obviously improved, but the cold start problem still exists.
The main structure and principle of the prior art are as follows:
1. a user-item attribute preference model is generated. The preference model of the user for the project attributes is the basis for user clustering and similarity calculation, and a preference weight matrix of the user for all attributes appearing in the project is established by analyzing a user-project scoring matrix and a project-attribute matrix.
2. And (5) clustering users. Clustering users by adopting a mixed clustering model combining SOM and K-means clustering:
2.1 taking the obtained user-item attribute preference matrix as input data of clustering, carrying out coarse clustering on input training for a few times through SOM, and outputting ClusterSOMWeight omega of neuronSOMThe number of clustering clusters K;
2.2 will ωSOMAs the original centroid OoriginalFor each cluster with element not 0, find and OoriginalThe closest element is taken as the final centroid O of the clusterSOM；
2.3 at K, OSOMAnd further clustering the users as the clustering cluster number and the initial clustering center of mass of the K-means cluster, and outputting a clustering result ClusterResult of the users.
3. User similarity calculation and nearest neighbor query. Calculating target user U by cosineiAnd the cluster cindexThe similarity of other users in the system is calculated to obtain a nearest neighbor set MKnear。
4. And (4) score prediction. Finding target user UiFor target item IijM of nearest neighbor usersKnearThen, through the set MKnearFor the target item IijWeighted average of scores to describe target user UiFor target item IijThe score of (1). The score prediction formula is shown as formula (1):
Figure GDA0001692096400000021
5. a recommendation is generated. Repeating the step (3) and the step (4), and predicting the target user UiSelecting N items with highest predicted scores to recommend to a target user U for the scores of all the unscored itemsi。
There are many recommendation algorithms at present, but there is no one algorithm that always outperforms the others in any context or under any data. The existing recommendation systems are mostly single methods, have self limitations and cannot be flexibly applied to various scenes. Algorithms that perform well in recommendation cannot effectively address data sparsity and cold start issues.
Disclosure of Invention
The invention provides an integration method for a financial product recommendation system, which reduces the influence caused by data sparsity and solves the cold start problem of the recommendation system. The adaptability of the recommendation algorithm is improved, and the application scene of the recommendation algorithm is expanded.
The collaborative filtering algorithm based on data smoothing can fill sparse data, and the problem of data sparsity is reduced. The demographic-based recommendation algorithm does not require historical data nor rely on the attributes of the items, and can solve the cold start problem of the user. The two algorithms are integrated with a recommendation algorithm which has good performance and is based on item clustering and matrix decomposition, so that the use scene of the recommendation algorithm is enlarged, and the adaptability of the recommendation algorithm is improved. The integration method can effectively reduce the sparsity of data, solve the problem of cold start and improve the recommendation performance of each user.
Drawings
FIG. 1 is a flow chart of an integration method for a financial product recommendation system according to the present invention.
Detailed Description
As shown in fig. 1, the present invention provides an integrated method for a financial product recommendation system, comprising the steps of:
the input data is: user characteristic information, a project-attribute matrix and a user-project scoring matrix; the output data is: a product recommendation model and a user recommendation result.
The method comprises the following steps: a demographic based recommendation algorithm.
The traditional recommendation algorithm based on demographics is improved, and different user attributes are endowed with different weights. The invention selects 4 characteristics of age, sex, occupation and hobby as the considered range, and preprocesses each attribute information into the form of digital representation. And calculating the similarity among the users to obtain the user preference.
1. Age attribute, the present invention is in 5 years increments, such as 32 years old may be scored as 7 and 56 years old as 12. And (3) calculating the similarity of the ages between the user s and the user t by using the Euclidean distance, wherein the similarity is shown as a formula (2).
Figure GDA0001692096400000041
Wherein A (s, t) representsSimilarity of user s and user t in age, xs、xtAge segmentation values for users s and t, respectively.
2. The distinction is a symmetric binary property, i.e. both states are equally important. The male is marked as 1, and the female is marked as 0, so that a list of two rows and two columns can be obtained according to the value of the gender of the user, as shown in table 1. Here, S (S, t) is used to represent the similarity between the user S and the user t in terms of gender, as shown in equation (3), where a is an attribute value in the case where both the object S and t take 1, b is an attribute value in the case where the object t takes 1 and the object S takes 0, c is an attribute value in the case where the object t takes 0 and the object S takes 1, and d is an attribute value in the case where both the object S and t take 0.
Table 1 binary attribute values
Figure GDA0001692096400000042
Figure GDA0001692096400000051
3. Occupation and hobby belong to the attribute of the label type, namely, the attribute is described by certain fields, and for the attribute, the similarity between the users s and t is calculated by using an equation (4). In the formula (4), k represents the number of tags having the same attribute and shared by the user s and t, and n represents the number of tags selectable for the attribute.
Figure GDA0001692096400000052
4. And calculating the similarity of the users. For user U and k neighbor sets UkEquation (5) calculates the information similarity of users and saves Top-N user sets U similar to the target user Uk. Under the user demographic feature method, the more features, the more accurate the user interest can be predicted.
Figure GDA0001692096400000053
Calculating user preferences
After the similarity between the users is obtained, K favorite articles of the users with the most similar interests to the users are recommended to the users, and the preference of the user u for the article i is calculated by adopting the following formula:
Figure GDA0001692096400000054
wherein S (u, K) includes K users closest to user u, N (i) is a set of users having past behavior on item i, wuvIs the interest similarity of user u and user v, rviIndicating the preference of user v for item i.
Deriving a prediction preference matrix
Figure GDA0001692096400000061
n is the number of items and m is the number of users.
Step two: recommendation algorithm based on item clustering and matrix decomposition
1. Calculating similarity between items
The distance between the items is calculated by using the manhattan distance.
Figure GDA0001692096400000062
ruiIndicating the user u's preference for item i. dijRepresenting the distance between item i and item j.
The similarity between the item i and the item j is expressed by equation (9).
Figure GDA0001692096400000063
ciIndicates the popularity of item i, cjIndicating the popularity of item j. Popularity of articlesThe degree is the number of people clicking on the item.
Then classifying the articles to obtain different clustering centers { c1,c2,Λ,ckAnd k is the number of clusters.
2. An item vector is constructed.
Based on K cluster centers, K is set to 200, and the item vector is defined as
Figure GDA0001692096400000064
Wherein the content of the first and second substances,
Figure GDA0001692096400000065
normalizing the article vector:
Figure GDA0001692096400000071
finally, the vector for item i is:
pi＝(pi1,pi2,Λ,pik,Λ,piK) (13)
wherein the content of the first and second substances,
Figure GDA0001692096400000072
3. a prediction preference matrix is calculated. Based on the commodity vector and Singular Value Decomposition (SVD), a prediction preference matrix can be derived:
Figure GDA0001692096400000073
n is the number of items, m is the number of users,
Figure GDA0001692096400000074
represents the prediction preference, defined as:
Figure GDA0001692096400000075
where allMean is the average of the preferences, buRepresenting the deviation between the user and the allMean, biRepresenting the deviation between the item and the allMean, quIs a vector of users u, initialized with random values.
Step three: collaborative filtering algorithms based on data smoothing.
1. And calculating the similarity of the users. The similarity is calculated using the pearson correlation coefficient.
The similarity between user u and user u' is:
Figure GDA0001692096400000081
Ru(t) represents a preference of user u for item t,
Figure GDA0001692096400000082
representing the average preference of user u for all items, Ru’(t) represents a preference of user u' for item t,
Figure GDA0001692096400000083
representing the average preference of user u 'for all items, and t is the item that user u and user u' both clicked on.
User defined as U ═ U1,u2,Λ,unDivide users into n clusters, denoted as
Figure GDA0001692096400000084
2. Based on the previous step, the data set that the user has not clicked on is smoothed. The user's preferences are expressed as:
Figure GDA0001692096400000085
wherein r isuiIs calculated by a function, and is obtained by calculating,
Figure GDA0001692096400000086
it is for i that user u has not clicked on that is derived from the smoothing.
For user u, the cluster to which u belongs is represented as
Figure GDA0001692096400000087
In consideration of individual differences, the calculation is performed by equation (19)
Figure GDA0001692096400000088
Figure GDA0001692096400000089
Figure GDA00016920964000000810
Is the average preference of all users for item i, calculated as follows:
Figure GDA00016920964000000811
wherein, Cu(i)∈CuIs represented in cluster CuUser set of item i clicked on, | Cu(i) I is represented in cluster CuThe number of users who clicked on item i.
The prediction preference may be derived by calculating a weighted sum:
Figure GDA0001692096400000091
Figure GDA0001692096400000092
is the preference of user u for item i,
Figure GDA0001692096400000093
is the average preference of the item i,
Figure GDA0001692096400000094
is the average preference of item j, wujIs the weight between u and j, and sim (i, j) is the similarity of i and j.
3. A prediction preference matrix is obtained.
Figure GDA0001692096400000095
n is the number of items and m is the number of users.
Step four: integrating algorithms
According to the first step, the second step and the third step, the prediction preference of the user for each product is obtained, and algorithm integration is carried out based on the preferences.
1. Linear weighted fusion method
Summarizing the results of a single model, then giving different weights according to different algorithms, and weighting the results of a plurality of recommendation algorithms to obtain the results:
Figure GDA0001692096400000096
wherein
Figure GDA0001692096400000097
Is the predicted final preference, wkIs the weight corresponding to the k algorithm.
2. Method of cross fusion
And inserting results of different recommendation models in the recommendation result to ensure the diversity of the results.
Figure GDA0001692096400000098
rec (u) denotes an item recommended to the user u, reck(u) represents the item recommended by algorithm k to user u.
3. Waterfall fusion method
The waterfall type fusion method adopts a method of connecting a plurality of models in series. Each recommendation algorithm is regarded as a filter and is performed by a method of linking filters with different granularities back and forth, in the method, the result filtered by the former recommendation method is input as a candidate set of the latter recommendation method, the candidate result is progressively selected in the process, and finally a recommendation result set with few high quality is obtained.

Claims (2)

1. An integration method for a financial product recommendation system, comprising the steps of:
the method comprises the following steps: a demographic-based recommendation algorithm;
selecting 4 characteristics of age, gender, occupation and hobby, preprocessing each attribute information into a digital representation form, calculating the similarity between users to obtain user preference and a prediction preference matrix;
step two: recommendation algorithm based on item clustering and matrix decomposition
Step 2.1, calculating similarity between articles
Calculating a distance between the articles by using the manhattan distance;
Figure FDA0002734133620000011
wherein r isuiIndicates the user u's preference for item i, dijRepresenting the distance between item i and item j,
the similarity between the item i and the item j is expressed by formula (9);
Figure FDA0002734133620000012
wherein, ciIndicates the popularity of item i, cjRepresents the popularity of item j; the popularity of an item is clicking on itThe number of people who have articles, then the articles are classified to obtain different clustering centers { c1,c2,…,ckK is the number of clusters;
step 2.2, construct the item vector
Based on K cluster centers, K is set to 200, and the item vector is defined as
Figure FDA0002734133620000013
Wherein the content of the first and second substances,
Figure FDA0002734133620000014
normalizing the article vector:
Figure FDA0002734133620000021
finally, the vector for item i is:
pi＝(pi1,pi2,…,pik,…,piK) (13)
wherein the content of the first and second substances,
Figure FDA0002734133620000022
step 2.3, calculating a prediction preference matrix
Based on the item vector and Singular Value Decomposition (SVD), a prediction preference matrix is obtained:
Figure FDA0002734133620000023
where n is the number of items, m is the number of users,
Figure FDA0002734133620000024
represents the prediction preference, defined as:
Figure FDA0002734133620000025
where allMean is the average of the preferences, buRepresenting the deviation between the user and the allMean, biRepresenting the deviation between the item and the allMean, quIs the vector of user u, initialized by random values;
step three: collaborative filtering algorithm based on data smoothing
Step 3.1, calculating the similarity of users
And calculating the similarity by adopting a Pearson correlation coefficient, wherein the similarity between the user u and the user u' is as follows:
Figure FDA0002734133620000026
wherein R isu(t) represents a preference of user u for item t,
Figure FDA0002734133620000031
representing the average preference of user u for all items, Ru’(t) represents a preference of user u' for item t,
Figure FDA0002734133620000032
representing the average preference of the user u 'for all items, and t is the item clicked by both the user u and the user u';
user defined as U ═ U1,u2,…,unDivide users into n clusters, denoted as
Figure FDA0002734133620000033
Step 3.2, smoothing the data set which is not clicked by the user based on the previous step
The user's preferences are expressed as:
Figure FDA0002734133620000034
wherein r isuiIs calculated by a function, and is obtained by calculating,
Figure FDA0002734133620000035
is derived from the smoothing for item i that user u has not clicked,
for user u, the cluster to which u belongs is represented as
Figure FDA0002734133620000036
In consideration of individual differences, the calculation is performed by equation (19)
Figure FDA0002734133620000037
Figure FDA0002734133620000038
Figure FDA0002734133620000039
Is the average preference of all users for item i, calculated as follows:
Figure FDA00027341336200000310
wherein, Cu(i)∈CuIs represented in cluster CuUser set of item i clicked on, | Cu(i) I is represented in cluster CuThe number of users who clicked on item i,
the prediction preference is obtained by calculating a weighted sum:
Figure FDA0002734133620000041
wherein the content of the first and second substances,
Figure FDA0002734133620000042
is the preference of user u for item i,
Figure FDA0002734133620000043
is the average preference of the item i,
Figure FDA0002734133620000044
is the average preference of item j, wujIs the weight between u and j, sim (i, j) is the similarity of i and j;
step 3.3, obtaining a prediction preference matrix
Figure FDA0002734133620000045
n is the number of items, m is the number of users;
step four: integrating algorithms
According to the first step, the second step and the third step, the prediction preference of the user for each product is obtained, and algorithm integration is carried out based on the preferences;
in the first step, the calculation process of the user preference is as follows:
after the similarity between the users is obtained, K favorite articles of the users with the most similar interests to the users are recommended to the users, and the preference of the user u for the article i is calculated by adopting the following formula:
Figure FDA0002734133620000046
wherein S (u, K) includes K users closest to user u, N (i) is a set of users having past behavior on item i, wuvIs the interest similarity of user u and user v, rviRepresenting a preference of user v for item i;
the calculation process of the prediction preference matrix is as follows:
Figure FDA0002734133620000051
where n is the number of items and m is the number of users.
2. The integration method for a financial product recommendation system according to claim 1, wherein step four is integrated by:
1) linear weighted fusion method
Summarizing the results of a single model, then giving different weights according to different algorithms, and weighting the results of a plurality of recommendation algorithms to obtain the results:
Figure FDA0002734133620000052
wherein the content of the first and second substances,
Figure FDA0002734133620000053
is the predicted final preference, wkIs the weight corresponding to the k algorithm;
2) method of cross fusion
The results of different recommendation models are interspersed in the recommendation result to ensure the diversity of the results,
Figure FDA0002734133620000054
wherein rec (u) denotes an item recommended to the user u, reck(u) represents the recommended items for user u by algorithm k;
3) waterfall type fusion method
The waterfall type fusion method adopts a method of connecting a plurality of models in series, each recommendation algorithm is regarded as a filter, and the method is carried out by connecting filters with different granularities back and forth.
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