CN109102127B - Commodity recommendation method and device - Google Patents

Commodity recommendation method and device Download PDF

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CN109102127B
CN109102127B CN201811011469.XA CN201811011469A CN109102127B CN 109102127 B CN109102127 B CN 109102127B CN 201811011469 A CN201811011469 A CN 201811011469A CN 109102127 B CN109102127 B CN 109102127B
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user
commodity
feature vector
preference
commodities
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CN109102127A (en
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李勇
郁佳杰
丁璟韬
张良伦
金德鹏
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Hangzhou Beigou Technology Co ltd
Tsinghua University
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Hangzhou Beigou Technology Co ltd
Tsinghua University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

Abstract

The embodiment of the invention provides a commodity recommendation method and a commodity recommendation device, which comprise the following steps: acquiring shopping data of a user group, training a prediction model constructed based on an eALS algorithm by using the shopping data, and acquiring a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity; for any user in the user group, the predicted preference of the user for all commodities is obtained according to the inner product of the user feature vector of the user and the feature vectors of all commodities, and the commodity recommendation list of the user is obtained according to the predicted preference of the user for all commodities. According to the embodiment of the invention, the influence of the commodity information concerned by the user is added into the eALS algorithm, so that the constructed prediction model can reflect the preference degree of the user to the commodity more truly, and a better recommendation effect is achieved.

Description

Commodity recommendation method and device
Technical Field
The embodiment of the invention relates to the technical field of hidden feedback recommendation, in particular to a commodity recommendation method and device.
Background
The difficulty of the implicit feedback recommendation system is the handling of unobserved data, and there are generally two methods for handling unobserved data: (1) all unobserved samples are treated as negative feedback based on an overall strategy, which has better convergence but generates a large number of inefficient negative samples; (2) and (3) sampling from an unobserved sample to obtain a negative feedback sample based on a sampling strategy, wherein the strategy can effectively reduce the number of negative samples during training, but the algorithm performance can be influenced.
The point-by-point Alternating Least square (eALS) method is a hidden feedback recommendation algorithm based on Matrix Factorization (MF). Optimization goals for etals are as follows:
Figure BDA0001785189620000011
wherein u and i represent user number, commodity number, pu、qiFeature vectors corresponding to the user and the commodity; r isuiRepresenting the real preference of a user u to a commodity i, usually marking a positive feedback sample as 1 and a negative sample as 0;
Figure BDA0001785189620000012
representing the prediction score of the model, and optimizing the prediction score to the real score ruiRegression; omegauiRepresenting the weight of the contribution of the commodity i purchased by the user u to the optimization result;
Figure BDA0001785189620000013
the term is used to prevent overfitting, λ controls the degree of overfitting.
In the general MF algorithm, the weight coefficient ωuiFor observationThe positive sample is 1 and all the samples not observed are assigned the same value less than 1, which has the advantage of reducing the complexity of calculation and storage, but has the disadvantage of not performing well on the real data set. eALS proposes a weight coefficient omega related to the popularity of a commodityuiThe method can describe the contribution of the unobserved samples to the optimization target more flexibly and accurately.
The unobserved samples consist of true negative feedback (the user has virtually no interest in purchasing) that should be given a greater weight ω, and missing values (the user may be interested in purchasing)ui. Considering that there is a difference in popularity of products, popular products have a greater likelihood of being known to the user, and if the user is not observed purchasing a popular product, the user has a greater likelihood of actually disliking the popular product than an unpopular product. Therefore, for the commodity with higher popularity, the corresponding unobserved behavior should have a greater weight, and the formula is described as follows:
Figure BDA0001785189620000021
wherein, ciRepresenting the corresponding weight when the commodity i is an unobserved sample;
Figure BDA0001785189620000022
representing the proportion of the purchased times of the commodity i to the purchased times of all commodities, and measuring the popularity of one commodity; r is purchase data; alpha is used to control the gap between popular and unpopular merchandise, 0<α<1, the difference between the two becomes smooth, α>At 1, the impact of popular goods is increased; c. C0For controlling the ratio of observed behavior to unobserved behavior weights. Restating the optimization objective of etals using the above formula is as follows:
Figure BDA0001785189620000023
disclosure of Invention
Embodiments of the present invention provide a method and apparatus for recommending a commodity, which overcome the above problems or at least partially solve the above problems.
According to a first aspect of embodiments of the present invention, there is provided a commodity recommendation method including:
the method comprises the steps of obtaining shopping data of a user group, wherein the shopping data comprise the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity;
training a prediction model constructed based on an eALS algorithm by using the shopping data to obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity;
for any user in the user group, the predicted preference of the user for all commodities is obtained according to the inner product of the user feature vector of the user and the feature vectors of all commodities, and the commodity recommendation list of the user is obtained according to the predicted preference of the user for all commodities.
According to a second aspect of the embodiments of the present invention, there is provided an article recommendation device including:
the shopping data acquisition module is used for acquiring the shopping data of a user group, wherein the shopping data comprises the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity;
the training module is used for training a prediction model constructed based on an eALS algorithm by using the shopping data to obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity;
and the recommending module is used for acquiring the predicted preference of the user to all the commodities according to the inner product of the user characteristic vector of the user and the characteristic vectors of all the commodities for any user in the user group, and acquiring a commodity recommending list of the user according to the predicted preference of the user to all the commodities.
According to a third aspect of the embodiments of the present invention, there is also provided an electronic apparatus, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, and the processor calls the program instructions to perform the merchandise recommendation method provided by any one of the various possible implementations of the first aspect.
According to a fourth aspect of the embodiments of the present invention, there is also provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the merchandise recommendation method provided in any one of the various possible implementations of the first aspect.
According to the commodity recommendation method and device provided by the embodiment of the invention, the influence of commodity information concerned by the user is added into the eALS algorithm, so that the constructed prediction model can reflect the preference degree of the user to the commodity more truly, and a better recommendation effect is achieved.
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Fig. 1 is a schematic flow chart of a commodity recommendation method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a merchandise recommendation device according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device provided in accordance with an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the drawings and examples. The following examples are intended to illustrate the examples of the present invention, but are not intended to limit the scope of the examples of the present invention.
In the existing hidden feedback prediction model, only behaviors (such as purchasing behaviors in commodity recommendation) which can directly reflect user preferences are considered. But in fact, the user has a great deal of additional attention behaviors in the process of purchasing the commodity, such as clicking, browsing, searching and other behaviors, and compared with purchasing behaviors, the behaviors reflect the preference of the user with lower credibility.
The inventive concept of the embodiment of the invention is as follows: the attention behaviors of the user are embodied in a prediction model based on the eALS algorithm, so that more accurate description of commodities preferred by the user is obtained. Fig. 1 is a schematic flowchart illustrating a product recommendation method according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
s101, acquiring shopping data of a user group, wherein the shopping data comprises the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity.
It should be noted that, first, shopping data of a user group is obtained through a database, where the shopping data is original data related to shopping, and includes a user number, a user purchase record and an attention record, in the embodiment of the present invention, a commodity in the attention record is a commodity which is only concerned but not purchased by the user by default, and a method for determining the user attention may be a behavior such as clicking, browsing, searching, and the like. The number of the commodity purchased by the user and the number of times are recorded in the purchase record, and the number of the commodity paid attention by the user is recorded in the attention record, for the purpose of convenient statistics, the number in the embodiment of the invention can be numbered from 1 by Arabic numerals, the number of the users in the user group is M, so the number of the users is from 1 to M, the general class of the commodity is N, and the number of the commodity can be from 1 to N.
It should be noted that the implicit feedback recommendation system regards the unobserved data and the observed data as negative feedback and positive feedback, respectively. In the embodiment of the invention, if the commodity is purchased by the user, the commodity is regarded as positive feedback, and the real preference of the user to the commodity is marked as 1; taking the commodity which is not concerned or purchased by the user as negative feedback, wherein the real preference of the user to the commodity is 0; compared with the concerned goods, the purchased goods suggest stronger user preference, and meanwhile, the user preference for the concerned goods should exceed the goods which are not concerned yet (the goods purchased by default in the embodiment of the present invention are certainly the concerned goods), so the user preference for the concerned goods should be between 0 and 1, and in the embodiment of the present invention, the true preference for the concerned goods is not determined.
S102, training a prediction model constructed based on an eALS algorithm by using shopping data, and obtaining a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity.
It should be noted that the performance of the etals algorithm is better than that of BPR (Bayesian Personalized Ranking model, Bayesian Personalized Ranking, sampling-based strategy), the prediction model constructed based on the etals algorithm uses the user feature vector representing the inherent attribute of the user and the commodity feature vector representing the inherent attribute of the commodity as arguments, the inner product of the user feature vector and the commodity feature vector is used for representing the preference degree of the user to the commodity as a dependent variable, the real preference of the user to partial commodities (purchased commodities and commodities which are not concerned and not purchased) and the real preference of the user to the concerned commodities are between the real preference of the purchased commodities and the real preference of the commodities which are not concerned and not purchased are taken as constraint conditions to be optimized, the concerned information of the user to the commodities is merged into the eALS algorithm, and the preference of the user to the commodities can be reflected more truly.
S103, for any user in the user group, the predicted preference of the user to all the commodities is obtained according to the inner product of the user feature vector of the user and the feature vectors of all the commodities, and the commodity recommendation list of the user is obtained according to the predicted preference of the user to all the commodities.
It should be noted that after the prediction model of the embodiment of the present invention is trained, a user feature vector representing the inherent attribute of each user and a commodity feature vector representing the inherent attribute of each commodity are actually obtained as much as possible, so that when the preference of any user in the user group needs to be predicted, the predicted preference of the user for all commodities can be obtained only by performing an inner product operation on the user feature vector of the user and all commodity feature vectors. After the predicted preference of the user for all the commodities is obtained, it is obvious that a certain number of recommended commodities can be obtained by sorting the predicted preference values.
According to the commodity recommendation method provided by the embodiment of the invention, the influence of commodity information concerned by the user is added into the eALS algorithm, so that the constructed prediction model can reflect the preference degree of the user to the commodity more truly, and a better recommendation effect is achieved.
The most intuitive idea to add the impact of the behavior of interest to the etals algorithm is to set the commodity of interest to the same regression value (between 0-1), but the same regression value oversimplifies the problem, making it difficult to determine the value size, and potentially negatively optimizing the algorithm performance. Therefore, embodiments of the present invention allow for the incorporation of the relative relationship between the purchased samples and the samples of interest (or the samples not of interest) in the predictive model, and the incorporation of such a relative relationship into the conventional etals algorithm can result in significant performance gains. Therefore, on the basis of the above embodiment, as an alternative embodiment, the expression of the prediction model J is:
Figure BDA0001785189620000061
r, V respectively represents a purchase commodity set and a focus commodity set; omegauiRepresenting the weight of the commodity i purchased by the user u in the optimization result; r isuiRepresenting the actual preference of the user u for the purchased goods i;
Figure BDA0001785189620000062
indicating the predicted preference of user u for purchased item i;
Figure BDA0001785189620000063
indicating that item j is neither purchased nor attended to by user u; lambda meterShowing regularization parameters that prevent overfitting; p is a radical ofuA feature vector representing user u; q. q.siA feature vector representing item i; m and N represent the total number of users and the total number of commodities, respectively; c. CjA weight coefficient indicating a popularity of the commodity j; svA weight coefficient indicating a popularity of the commodity v;
Figure BDA0001785189620000071
representing the predicted preferences of user u for item v;
Figure BDA0001785189620000072
representing the predicted preference of user u for item j; gamma ray1To represent
Figure BDA0001785189620000073
The expected value of (d); gamma ray2To represent
Figure BDA0001785189620000074
Is calculated from the expected value of (c).
It should be noted that in the prediction model
Figure BDA0001785189620000075
Figure BDA0001785189620000076
Partially regarded as JeALSItem, will
Figure BDA0001785189620000077
Partially regarded as JRegItem, will
Figure BDA0001785189620000078
Figure BDA0001785189620000079
Partially regarded as JviewAn item. Comparing the predictive models of the embodiments of the present invention with the prior art, JeALSItem and JRegItems are the same as in the predictive models of the prior art, and are used to control the predictive scores and scores of purchased goods, respectivelyControlling overfitting to increase JviewThe terms are used to control the prediction score of the commodity of interest.
In the prediction model J, ωui、λ、γ1And gamma2Lambda is used to control the degree of overfitting for the pre-determined parameters prior to training, as can be seen from the above embodiment, the predicted preference of the predicted user u for the commodity of interest v
Figure BDA00017851896200000710
Should be between the purchased goods i and other goods j that have not been purchased and are not of interest, specifically, considered to be
Figure BDA00017851896200000711
And
Figure BDA00017851896200000712
with a phase difference of gamma1At the same time
Figure BDA00017851896200000713
And
Figure BDA00017851896200000714
with a phase difference of gamma2Respectively using
Figure BDA00017851896200000715
And
Figure BDA00017851896200000716
a formal cost function indicates that, when the two equations take 0,
Figure BDA00017851896200000717
and the preference precedence relationship of the assumed purchasing behavior, the attention behavior and other unobserved interactive behaviors of the user is met.
Figure BDA00017851896200000718
The influence of all users and the commodities (u, V) epsilon V) concerned by the users in the user-commodity preference characterization is considered.
Parameter svOf (1) containsMeaning and cj(weight corresponding to the case where the product is neither purchased nor paid attention to, formula for calculation, and c in the background artiThe same formula) are similar, and represent the weight corresponding to the concerned commodity, the parameter svThe expression of (a) is:
Figure BDA0001785189620000081
s0for controlling the ratio of the weight of the goods of interest to that of the goods of no interest, 0<b<1, the difference between the two becomes smooth, b>At 1, the impact of the popular goods will be enhanced,
Figure BDA0001785189620000082
indicating the ratio of the number of times of interest of the product v to the number of times of interest of all the products. Obviously, in the prediction model J, cjAnd svAre all known parameters.
As can be seen from the conventional algorithm for eALS,
Figure BDA0001785189620000083
representing the prediction score of the model, and optimizing the prediction score to the real score ruiIt is to be understood that the embodiments of the present invention are illustrative only
Figure BDA0001785189620000084
That is, when the predicted preference of the product v concerned by the user u is calculated, the user feature vector p of the user u is useduThe commodity feature vector q associated with the commodity vvWhen the predicted preference of the commodity j which is neither concerned nor purchased by the user u is calculated as a result of the inner product of (a), the user feature vector p of the user u is useduThe commodity feature vector q with commodity jjThe inner product of (d) is the result.
Based on the content of the foregoing embodiment, as an optional embodiment, the obtaining of the product recommendation list of the user according to the predicted preference of the user for all products specifically includes:
sorting all commodities according to the predicted preference of the user from big to small to obtain a first commodity list; deleting the commodities purchased by the user from the first commodity list to obtain a second commodity list; and sequentially selecting a preset number of commodities from the second commodity list from front to back to form a commodity recommendation list of the user.
It should be noted that, in the embodiment of the present invention, the predicted preferences of all the commodities are sorted from large to small, and then the commodities purchased by the user are deleted from the first commodity list, so that a second commodity list sorted according to the predicted preferences and the commodities not purchased by the user at present can be obtained, and then the preset number is set to screen from front to back from the second commodity list, so that the commodity recommendation list finally facing the user can be obtained.
Based on the content of the foregoing embodiment, as an optional embodiment, the shopping data is used to train a prediction model constructed based on the eALS algorithm, and a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution are obtained, which specifically includes:
for the currently optimized user feature vector puComponent p of the f-th dimensionufWith the component pufFor the derivation of the prediction model, the component p when the derivation result is 0 is setufAs an optimized component pufFrom the optimized component pufUpdate the prediction model and continue on component puf+1Optimizing until the user characteristic vector puAll the components are optimized;
when the user feature vector puAfter all the components are optimized, continuing to perform pu+1Optimizing until all the user feature vectors are optimized, and optimizing the commodity vectors after all the user feature vectors are optimized;
for the currently optimized commodity vector qiComponent q of the f-th dimensionifWith a component qifDerivation of the prediction model, a component q when the derivation result is 0ifAs a result of the optimization of the component qifFrom the optimized component qifUpdate the prediction model and continue on component qif+1Optimizing until the commodity vector qiOfFinishing component optimization;
when the user feature vector qiAfter all the components are optimized, continuing to pair qi+1And optimizing until all commodity vectors are optimized.
It should be noted that, in the embodiment of the present invention, when the prediction model is trained, the feature vectors of the users are optimized first, and after all the feature vectors of the users are optimized, the feature vectors of the commodities are optimized.
The time complexity of the conventional eALS method is O ((M + N) K)2+ | R | K), M, N, K represents total number of users, total number of goods, feature vector dimensions, respectively, | R | represents the size of the purchase set; after the action influence item is added, the traditional eALS method still needs extra O (| V | NK) time cost, and the extra time cost is far larger than the original cost. This section, however, contains a large number of duplicate calculations and can be accelerated in the following manner.
With the user feature vector puComponent p of (1)ufFor example, derivation of the prediction model is obtained;
Figure BDA0001785189620000101
Figure BDA0001785189620000102
calculation of the terms:
Figure BDA0001785189620000103
in the above formula
Figure BDA0001785189620000104
Indicating the prediction score after the f-th dimension of the feature vector is removed. For a complete iteration (updating the user feature vectors and commodity feature vectors of all users once), the complexity of the direct computational formula is O (MNK)2)。
Will be provided with
Figure BDA0001785189620000105
The transformation is:
Figure BDA0001785189620000106
due to the fact that
Figure BDA0001785189620000107
The items are the same for all users, and
Figure BDA0001785189620000108
terms need only be calculated once for different dimensions, and therefore can be pre-calculated before derivation
Figure BDA0001785189620000109
The term, so that the result of the term can be reused in the derivation, K terms are calculated before each iteration
Figure BDA00017851896200001010
The corresponding temporal complexity is: o (nk), substantially reduced computational complexity.
While
Figure BDA00017851896200001011
The time complexity required for the direct computation of the term for one user is O (NK)2) Will be
Figure BDA00017851896200001012
The transformation is:
Figure BDA00017851896200001013
p between different dimensionsu*qi TOnly need to calculate once, only RuThe term can be different from that of O (K)2+|RuI K) the term is calculated over time. Therefore, the temperature of the molten metal is controlled,
Figure BDA00017851896200001014
the time complexity of the term can be represented by O (MNK)2) Reduced to O ((M + N) K)2+|R|K)。
For the
Figure BDA0001785189620000111
Calculation of the terms:
Figure BDA0001785189620000112
obviously, the regularization term does not pose a challenge to the temporal complexity of the algorithm, only yielding a total temporal overhead of O ((M + N) K).
For the
Figure BDA0001785189620000113
Calculation of the terms:
Figure BDA0001785189620000114
in the above formula, the term (e) is
Figure BDA0001785189620000115
(f) The item is
Figure BDA0001785189620000116
Figure BDA0001785189620000117
Since the terms (b), (d) and (f) all relate to the pairs
Figure BDA0001785189620000118
The range calculation requires traversal of (V, j), thus resulting in a time overhead of O (| V | N).
Acceleration is illustrated by taking item (d) as an example
Figure BDA0001785189620000119
A method of computation of an item.
Item (d) is first split into the following forms,
Figure BDA00017851896200001110
that is, the original (d) term is split from traversal (v, j) into the product of two independent traversal v and traversal j terms. Wherein the time complexity of traversing V is O (| V)uAnd traversing j is equivalent to traversing all items (because purchase and click behavior is sparse), the time complexity is o (n), and further, traversing j can do the following:
Figure BDA0001785189620000121
Figure BDA0001785189620000122
attention is drawn from the above equation
Figure BDA0001785189620000123
And
Figure BDA0001785189620000124
the two terms are irrelevant to the user, and only need to be calculated once before all the users are updated, so that the term (d) only needs O (K + | RV) finallyu|), and further,
Figure BDA0001785189620000125
term time overhead is reduced from O (| V | N) to O ((M + N) K)2+|V|K)。
According to another aspect of the embodiment of the present invention, there is also provided an article recommendation device, referring to fig. 2, fig. 2 shows a functional block diagram of the article recommendation device provided according to the embodiment of the present invention; the device is used for the method for recommending the commodities according to the commodities purchased and concerned by the user in the embodiments. Therefore, the description and definition in the commodity recommendation method in the foregoing embodiments may be used for understanding each execution module in the embodiments of the present invention.
As shown in fig. 2, the apparatus includes:
the shopping data acquisition module 201 is configured to acquire shopping data of a user group, where the shopping data includes a number of each user in the user group, a number of a commodity purchased by the user, a number of a commodity concerned, and a tag of a real preference of the user to the commodity, and the tag is used to represent whether the user has purchased the commodity;
a training module 202, configured to train a prediction model constructed based on the etals algorithm using the shopping data, and obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity;
the recommending module 203 is configured to, for any one user in the user group, obtain the predicted preference of the user for all the commodities according to the inner product of the user feature vector of the user and the feature vectors of the commodities, and obtain a commodity recommendation list of the user according to the predicted preference of the user for all the commodities.
According to the commodity recommendation device provided by the embodiment of the invention, the influence of commodity information concerned by the user is added into the eALS algorithm, so that the constructed prediction model can reflect the preference degree of the user to the commodity more truly, and a better recommendation effect is achieved.
FIG. 3 illustrates a block diagram of an electronic device provided in accordance with an embodiment of the invention, such as the processor (processor)301, the memory (memory)302, and the bus 303 shown in FIG. 3;
the processor 301 and the memory 302 respectively complete communication with each other through a bus 303; the processor 301 is configured to call the program instructions in the memory 302 to execute the control method provided by the above embodiment, for example, including: the method comprises the steps of obtaining shopping data of a user group, wherein the shopping data comprise the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity; training a prediction model constructed based on an eALS algorithm by using shopping data to obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity; for any user in the user group, the predicted preference of the user for all commodities is obtained according to the inner product of the user feature vector of the user and the feature vectors of all commodities, and the commodity recommendation list of the user is obtained according to the predicted preference of the user for all commodities.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, where the computer instructions cause a computer to execute the control method provided in the foregoing embodiment, for example, including: the method comprises the steps of obtaining shopping data of a user group, wherein the shopping data comprise the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity; training a prediction model constructed based on an eALS algorithm by using shopping data to obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity; for any user in the user group, the predicted preference of the user for all commodities is obtained according to the inner product of the user feature vector of the user and the feature vectors of all commodities, and the commodity recommendation list of the user is obtained according to the predicted preference of the user for all commodities.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and not to limit the same; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A method for recommending an article, comprising:
the method comprises the steps of obtaining shopping data of a user group, wherein the shopping data comprise the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity;
training a prediction model constructed based on an eALS algorithm by using the shopping data to obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity;
for any user in the user group, obtaining the prediction preference of the user to all commodities according to the inner product of the user characteristic vector of the user and the characteristic vectors of all commodities, and obtaining a commodity recommendation list of the user according to the prediction preference of the user to all commodities;
wherein the expression of the prediction model is:
Figure FDA0003110453190000011
wherein i, v and j represent the commodity purchased by the user u, the concerned commodity and the unfocused commodity respectively; r and V respectively represent a set of purchased commodities and a set of concerned commodities; omegauiRepresenting the weight of the commodity i purchased by the user u in the optimization result; r isuiAnd
Figure FDA0003110453190000012
respectively representing the real preference and the predicted preference of the user u to the purchased commodity i; λ represents a regularization parameter that prevents overfitting; p is a radical ofuA feature vector representing user u; q. q.siA feature vector representing item i; m and N represent the total number of users and the total number of commodities, respectively; c. CjA weight coefficient indicating a popularity of the commodity j; svA weight coefficient indicating a popularity of the commodity v;
Figure FDA0003110453190000013
representing the predicted preferences of user u for item v;
Figure FDA0003110453190000014
representing the predicted preference of user u for item j; gamma ray1To represent
Figure FDA0003110453190000015
The expected value of (d); gamma ray2To represent
Figure FDA0003110453190000021
Is calculated from the expected value of (c).
2. The method according to claim 1, wherein said obtaining a recommended list of goods for the user based on the predicted preferences of the user for all goods comprises:
sorting all commodities according to the predicted preference of the user from big to small to obtain a first commodity list;
deleting the commodities purchased by the user from the first commodity list to obtain a second commodity list;
and sequentially selecting a preset number of commodities from the second commodity list from front to back to form a commodity recommendation list of the user.
3. The method as claimed in claim 1, wherein the training of the prediction model constructed based on the etas algorithm by using the shopping data to obtain the user feature vector of each user and the commodity feature vector of each commodity when the prediction model reaches the optimal solution is specifically as follows:
for the currently optimized user feature vector puComponent p of the f-th dimensionufWith the component pufFor the derivation of the prediction model, the component p when the derivation result is 0 is setufAs an optimized component pufFrom the optimized component pufUpdate the prediction model and continue on component puf+1Optimizing until the user characteristic vector puAll the components are optimized;
when the user feature vector puAfter all the components are optimized, continuing to perform pu+1Optimizing until all the user feature vectors are optimized, and optimizing the commodity vectors after all the user feature vectors are optimized;
for the currently optimized commodity vector qiComponent q of the f-th dimensionifWith a component qifDerivation of the prediction model, a component q when the derivation result is 0ifAs a result of the optimization of the component qifFrom the optimized component qifUpdate the prediction model and continue on component qif+1Optimizing until the commodity vector qiAll the components are optimized;
when the user feature vector qiAfter all the components are optimized, continuing to pair qi+1And optimizing until all commodity vectors are optimized.
4. An article recommendation device, comprising:
the shopping data acquisition module is used for acquiring the shopping data of a user group, wherein the shopping data comprises the number of each user in the user group, the number of a commodity purchased by the user, the number of a concerned commodity and a label of the real preference of the user to the commodity, and the label is used for representing whether the user purchases the commodity;
the training module is used for training a prediction model constructed based on an eALS algorithm by using the shopping data to obtain a user feature vector of each user and a commodity feature vector of each commodity when the prediction model reaches an optimal solution; the inner product of the user feature vector and the commodity feature vector is used for representing the prediction preference of the user on the commodity;
the recommendation module is used for acquiring the prediction preference of the user on all commodities according to the inner product of the user characteristic vector of the user and each commodity characteristic vector for any user in the user group, and acquiring a commodity recommendation list of the user according to the prediction preference of the user on all commodities;
wherein the expression of the prediction model is:
Figure FDA0003110453190000031
wherein i, v and j represent the commodity purchased by the user u, the concerned commodity and the unfocused commodity respectively; r and V respectively represent a set of purchased commodities and a set of concerned commodities; omegauiRepresenting the weight of the commodity i purchased by the user u in the optimization result; r isuiAnd
Figure FDA0003110453190000032
respectively representing the real preference and the predicted preference of the user u to the purchased commodity i; λ represents a regularization parameter that prevents overfitting; p is a radical ofuA feature vector representing user u; q. q.siA feature vector representing item i; m and N represent the total number of users and the total number of commodities, respectively;cja weight coefficient indicating a popularity of the commodity j; svA weight coefficient indicating a popularity of the commodity v;
Figure FDA0003110453190000033
representing the predicted preferences of user u for item v;
Figure FDA0003110453190000034
representing the predicted preference of user u for item j; gamma ray1To represent
Figure FDA0003110453190000035
The expected value of (d); gamma ray2To represent
Figure FDA0003110453190000041
Is calculated from the expected value of (c).
5. An electronic device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 3.
6. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1 to 3.
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