CN113158024A - Causal reasoning method for correcting popularity deviation of recommendation system - Google Patents
Causal reasoning method for correcting popularity deviation of recommendation system Download PDFInfo
- Publication number
- CN113158024A CN113158024A CN202110218946.5A CN202110218946A CN113158024A CN 113158024 A CN113158024 A CN 113158024A CN 202110218946 A CN202110218946 A CN 202110218946A CN 113158024 A CN113158024 A CN 113158024A
- Authority
- CN
- China
- Prior art keywords
- user
- item
- recommendation system
- score
- popularity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a causal reasoning method for correcting popularity deviation of a recommendation system, which comprises the following steps: acquiring a matching score of a user and an article in a current recommendation system; predicting an item score according to the popularity of the item, and predicting a user score according to the preference of the user; and aggregating the matching scores of the user and the articles, the article scores and the user scores, predicting the matching scores of the user and the articles, and removing the influence caused by the popularity deviation to obtain the final matching scores of the user and the articles. The method provided by the invention is a model-independent counterfactual reasoning framework, can be suitable for various recommendation systems, improves the recommendation performance of the recommendation system by eliminating the popularity deviation, and can provide better and accurate personalized recommendation content for users.
Description
Technical Field
The invention relates to the technical field of personalized recommendation, in particular to a causal reasoning method for correcting popularity deviation of a recommendation system.
Background
Personalized recommendations have revolutionized many online applications, such as e-commerce, search engines, and conversational systems. A number of recommendation models have been developed in which the default optimization option is to reconstruct the user-item historical interactions. However, the frequency distribution of items in the interaction data is never uniform, and is influenced by many factors, such as exposure mechanism, public praise effect, sales activity, product quality, and the like. In most cases, the frequency distribution of the items is long-tailed, i.e., a few popular items have a significant fraction of the interactions in the data set. This biases the classic training paradigm towards recommending popular items, rather than revealing the true preferences of the user.
The general goal of recommendation systems is to provide personalized suggestions to users, rather than simply recommending popular items. While the conventional training paradigm, which fits user behavior data by training a recommendation model, may bias the model towards popular items. This results in a poor margarian effect, making popular items recommended more frequently and therefore more popular. Existing research attempts to solve this problem by reducing the impact of popular items on training while increasing long tail item weight using inverse trend weighting (IPW). Although this approach is reasonable in theory, its weighting strategy is highly sensitive, which can lead to difficulties in tuning.
Disclosure of Invention
The invention aims to provide a causal reasoning method for correcting popularity deviation of a recommendation system, which is a model-independent counterfactual reasoning framework and can be suitable for various recommendation systems.
The purpose of the invention is realized by the following technical scheme:
a causal reasoning method for correcting popularity deviations of a recommendation system comprises the following steps:
acquiring a matching score of a user and an article in a current recommendation system;
predicting an item score according to the popularity of the item, and predicting a user score according to the preference of the user;
and aggregating the matching scores of the user and the articles, the article scores and the user scores, predicting the matching scores of the user and the articles, and removing the influence caused by the popularity deviation to obtain the final matching scores of the user and the articles.
The technical scheme provided by the invention can show that 1) the scheme is independent of the model (namely, the recommendation system model) and can be simply applied to any recommendation system model. 2) Is insensitive to the hyper-parameters, and reduces the time and difficulty for adjusting the parameters. 3) The recommendation performance of the recommendation system can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a schematic diagram of a cause and effect diagram of a recommendation system provided by an embodiment of the present invention;
FIG. 2 is a flowchart of a causal inference method for correcting popularity bias of a recommendation system according to an embodiment of the present invention;
FIG. 3 is a comparison of recommended performance on an Adressa data set according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating an influence of the parameter c on the recommended performance according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discusses the problem of popularity deviation from a novel and basic view, namely causal relation, and provides a causal reasoning method for correcting the popularity deviation of a recommendation system, wherein the causal reasoning method is a model-independent counterfactual reasoning method framework (MACR). Research has found that popularity bias exists in the direct impact from item nodes to ranking scores, so the intrinsic properties of an item are the reason for falsely assigning them a higher ranking score. To eliminate the prejudice of popularity, it is necessary to think of the question of the counterfactual that if a model uses only the relevant information of an item, then what its ranking score will be. Therefore, a causal graph is constructed to describe important causal relationships in the recommendation process, as shown in fig. 1, the causal graph is a recommendation system causal graph, and the causal relationships among the popularity of the modeled items, the activity of the user and the user provided by the traditional recommendation system are described; in fig. 1, I and U are nodes in a causal graph, and abstractly represent items and users of a recommendation system, directed edges of different nodes represent causal relationships between the different nodes, k (knowledge) represents knowledge information extracted from integrated user and item information, and Y represents a recommendation score (i.e., a score for recommendation) finally given to the item node U corresponding to the current user node I by the recommendation system. In training, the contribution of each causal relationship to the recommendation score is learned through multitask learning. In the testing process, the influence of popularity is eliminated through counterfactual reasoning. It is noted that the solution provided by the invention modifies the learning process of the recommender system, which is common to many recommender systems-it can be easily implemented in existing methods.
As shown in fig. 2, a main flow of a causal inference method for correcting popularity deviation of a recommendation system according to an embodiment of the present invention includes:
1. and acquiring the matching score of the user and the item in the current recommendation system.
The current recommendation system can be various existing recommendation systems, the matching score of the user and the object can directly use the ranking score in the existing recommendation system, the ranking score is that the recommendation system takes the object and the representation information of the user as input, and the calculated score can reflect the preference of the user to which degree the object can be matched.
2. Predicting an item score according to the popularity of the item, and predicting a user score according to the user's preference.
In the embodiment of the present invention, an article modeling module and a user modeling module may be provided to predict the article score and the user score, respectively, and the following main descriptions are provided:
an item modeling module:
indicating the impact of popularity of the item, more popular items are scored higher.
A user modeling module:
showing how much user u will interact with the item regardless of whether the user item matches or not. Considering that two users are randomly recommended the same number of videos, one user may click on more videos because of broader preference or stronger activity. Such users with wide interest will get higher
In the embodiment of the present invention, I and U each indicate a current item and user in the recommendation system, and I ═ I and U ═ U indicate that the current item and user are specific items I and users U. In the two modeling modules, information (such as attributes or historical purchase records) of articles and users is used as input, and then Y is calculated through a neural network respectivelyiAnd YuThe specific computational scheme may be implemented by conventional techniques,for example, a single-layer fully-connected neural network is used for calculation.
3. And aggregating the matching scores of the user and the articles, the article scores and the user scores, predicting the matching scores of the user and the articles, and removing the influence caused by the popularity deviation to obtain the final matching scores of the user and the articles.
Aggregating the matching scores of the users and the items, the item scores and the user scores, and predicting that the matching scores of the users and the items are expressed as follows:
wherein the content of the first and second substances,
representing the predicted matching score of the user u and the item i;
represents the matching score (ranking score) of the user u with the item i in the current recommendation system,
each represents a predicted user score and item score; sigma (.) represents a sigmoid function by which
And
the change is in [0,1 ]]The click probability of the range of (a) to adjust the degree of dependence on user-item matching.
The key to eliminating the prevalence bias is by eliminating the direct impact of the article on the model, and therefore, will be
Subtracting the influence caused by the popularity deviation to obtain a final matching score:
wherein c is a hyper-parameter, representing
The actual counter-fact state of (a) can be set according to actual conditions or experience, and the invention does not limit the specific numerical values.
Through the method, the final matching score between each user and different articles can be obtained for each user, the recommendation list is generated based on the final matching scores of the users and the different articles, and the recommendation list is recommended to the users; illustratively, according to the set recommended item number K, K items with the maximum final matching score can be selected, and are recommended to the user after being sorted in a descending order; for example, K may be set to 20, and the specific value of K may be set according to actual circumstances or experience, and the specific value of K is not limited in the present invention.
The scheme provided by the embodiment of the invention can realize the recommendation of the de-bias by matching with multi-task learning to carry out model training and counterfactual reasoning on any existing recommendation system, and only one user modeling module Y is required to be added to the existing recommendation systemu(U) and an item modeling Module Yi(I) In that respect These modules incorporate users and items into the recommendation score, which may be implemented using a fully connected network, for example.
In the embodiment of the invention, an article modeling module, a user modeling module and a current recommendation system are used as a model for training. Applications of
As a predicted recommendation score and using a loss function of the recommendation system, a multi-task learning mode is also designed for modeling the user and project modules
And
an additional constraint is added, in the form of a loss function for the training phase:
L＝LO+α*LI+β*LU
wherein alpha and beta are hyper-parameters used for adjusting the balance among different tasks; l isOA loss function, L, representing the current recommendation systemILoss function, L, representing an item modeling moduleURepresents the loss function of the user modeling module, by LIAnd LUAnd achieving the optimization goal of modeling the causal relationship.
The loss function of the current recommendation system is expressed as:
the loss function of the user modeling module is represented as:
the loss function of the item modeling module is represented as:
wherein, (u, i) represents a user-item pair consisting of a user u and an item i, and D represents training data comprising a plurality of user-item pairs; y isuiIs the real interaction data (real matching score) in the training data, and a value of 1 represents that there is interaction between the user u and the item i, otherwise, it is 0.
Training the goal to fit the true historical interaction yuiUntil a training termination condition is met (for example, the training times reach the standard, or the loss function of continuous training times does not decrease, etc.); then, can pass throughAnd (4) calculating the final matching score of the user and the article in the manner introduced in the steps 1-3, and recommending.
In order to illustrate the effectiveness of the above scheme of the embodiment of the present invention, in the existing recommendation system model: experiments were performed on the matrix factorization Model (MF) and the lightweight map recommendation model (LightGCN), which are representative of the traditional recommendation system and the most advanced recommendation model, respectively, using the five real datasets shown in table 1.
| Data set | User' s | Article with a cover | Number of interactions | Degree of sparseness |
| Adressa | 13,485 | 744 | 116,321 | 0.011594 |
| Globo | 158,323 | 12,005 | 2,520,171 | 0.001326 |
| ML10M | 69,166 | 8,790 | 5,000,415 | 0.008225 |
| Yelp | 31,668 | 38,048 | 1,561,406 | 0.001300 |
| Gowalla | 29,858 | 40,981 | 1,027,370 | 0.000840 |
Table 1 data set information statistics
1. The test precision for an unbiased scene is higher: according to the method, a plurality of causal relationships are modeled in the training process by introducing causal inference, and the expressive ability of the model in an unbiased scene is improved. Table 2 shows the results of the performance comparison.
TABLE 2 comparison of Performance of MF with MACR _ MF
The MACR _ MF in table 2 indicates that the solution provided by the present invention is applied to the MF model, i.e. the current system is the MF model as described above. R is called Recall, represents the Recall rate, and is an index for measuring the accuracy rate of recommendation; n represents NDCG (Normalized discrete cumulative gain), which is an index for measuring the sequencing effect; HR is collectively referred to as Hit Ratio and represents the Hit rate, i.e., the probability that a recommendation hits the user's interest. As can be seen from Table 2, MACR _ MF performs better than the MF model on datasets of varying data volumes and data densities.
2. The method of the present invention is model independent and can be applied to any recommendation system model, and in table 2 above, the effect of the present invention is verified on the MF model; the effect on the lightweight graph recommendation model (LightGCN) is further demonstrated below, and the effect on recommendation lists of different sizes is demonstrated, as shown in fig. 3.
In fig. 3, MACR _ LightGCN indicates that the solution provided by the present invention is applied to the LightGCN model, i.e. the current system is the LightGCN model as described above. In FIG. 3, the three parts (a), (b) and (c) are the results of the verification on the indices HR @ K, NDCG @ K and Recall @ K in turn, and four lines in the three parts correspond to MACR _ LightGCN, MACR _ MF, LightGCN and MF in turn from top to bottom. It can be seen from fig. 3 that the present invention can achieve good effects when applied to different inference system models.
3. Is insensitive to the hyper-parameters, and reduces the time and difficulty for adjusting the parameters.
As shown in fig. 4, which demonstrates the effect on the results on the Adressa dataset with respect to parameter c. Compared with a LighgCN model and an MF model, the effect of the invention can be continuously improved, and the parameter is easy to adjust, and the effect can be very good by simply adjusting to about 30.
Those skilled in the art will understand that the value of the end part of each index represents the number of recommended items for each user, for example, R @20 represents that each user recommends 20 items, then calculates the Recall index (the size of the index has a relationship with how many recommended items), and the subsequent "K" is also similar meaning, i.e. represents that each user recommends K items, then calculates the Recall index, and so on.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A causal reasoning method for correcting popularity bias of a recommendation system, comprising:
acquiring a matching score of a user and an article in a current recommendation system;
predicting an item score according to the popularity of the item, and predicting a user score according to the preference of the user;
and aggregating the matching scores of the user and the articles, the article scores and the user scores, predicting the matching scores of the user and the articles, and removing the influence caused by the popularity deviation to obtain the final matching scores of the user and the articles.
2. The causal reasoning method for correcting the popularity bias of a recommendation system according to claim 1, wherein the predicting of the item score according to the popularity of the item and the predicting of the user score according to the preference of the user are respectively realized by an item modeling module and a user modeling module;
training an article modeling module, a user modeling module and a current recommendation system as a model, wherein a loss function is as follows:
L＝LO+α*LI+β*LU
wherein alpha and beta are hyper-parameters; l isOA loss function, L, representing the current recommendation systemILoss function, L, representing an item modeling moduleURepresenting the loss function of the user modeling module.
3. The causal reasoning method for correcting the popularity deviation of the recommendation system according to claim 2, wherein the penalty function of the current recommendation system is expressed as:
wherein, (u, i) represents a user-item pair consisting of a user u and an item i, and D represents training data comprising a plurality of user-item pairs;
representing the matching score of the user u and the item i in the current recommendation system, and sigma (.) representing a sigmoid function; y isuiThe data are real interactive data in the training data, the value is 1, which represents that the interaction exists between the user u and the article i, otherwise, the value is 0.
4. The causal reasoning method for correcting the recommendation system popularity bias of claim 2, wherein the loss function of the user modeling module is expressed as:
wherein, (u, i) represents a user-item pair consisting of a user u and an item i, and D represents training data comprising a plurality of user-item pairs;
representing the predicted user score, and sigma (.) representing a sigmoid function; y isuiThe data are real interactive data in the training data, the value is 1, which represents that the interaction exists between the user u and the article i, otherwise, the value is 0.
5. The causal reasoning method for correcting recommendation system popularity bias of claim 2, wherein the loss function of the item modeling module is expressed as:
wherein, (u, i) represents a user-item pair consisting of a user u and an item i, and D represents training data comprising a plurality of user-item pairs;
represents the predicted item score, σ (.) represents the sigmoid function; y isuiThe data are real interactive data in the training data, the value is 1, which represents that the interaction exists between the user u and the article i, otherwise, the value is 0.
6. The causal reasoning method for correcting recommendation system popularity bias of claim 1, wherein said aggregating user-to-item matching scores, item scores, and user scores predicts that a user-to-item matching score is expressed as:
wherein the content of the first and second substances,
representing the predicted matching score of the user u and the item i;
represents the matching score of the user u and the item i in the current recommendation system,
each represents a predicted user score and item score; σ (.) denotes a sigmoid function.
7. The causal reasoning method for correcting the popularity deviation of the recommendation system according to claim 1 or 6, wherein the final matching score of the user and the item is calculated by the following formula:
wherein the content of the first and second substances,
represents the matching score of the user u and the item i in the current recommendation system,
each represents a predicted user score and item score; σ (.) denotes a sigmoid function; c is a hyper-parameter, representing
Counter fact state of (c).
8. The causal reasoning method for correcting popularity bias of recommendation systems according to claim 1, wherein recommendation lists are generated based on the size of the final matching scores of the user and different items and recommended to the user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110218946.5A CN113158024B (en) | 2021-02-26 | 2021-02-26 | Causal reasoning method for correcting popularity deviation of recommendation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110218946.5A CN113158024B (en) | 2021-02-26 | 2021-02-26 | Causal reasoning method for correcting popularity deviation of recommendation system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113158024A true CN113158024A (en) | 2021-07-23 |
| CN113158024B CN113158024B (en) | 2022-07-15 |
Family
ID=76883537
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110218946.5A Active CN113158024B (en) | 2021-02-26 | 2021-02-26 | Causal reasoning method for correcting popularity deviation of recommendation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113158024B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114090890A (en) * | 2021-11-23 | 2022-02-25 | 电子科技大学 | Counterfactual project recommendation method based on graph convolution network |
| CN114861783A (en) * | 2022-04-26 | 2022-08-05 | 北京三快在线科技有限公司 | Recommendation model training method and device, electronic equipment and storage medium |
| CN115809374A (en) * | 2023-02-13 | 2023-03-17 | 四川大学 | Method, system, device and storage medium for correcting mainstream deviation of recommendation system |
| CN117522532A (en) * | 2024-01-08 | 2024-02-06 | 浙江大学 | Popularity deviation correction recommendation method and device, electronic equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103209342A (en) * | 2013-04-01 | 2013-07-17 | 电子科技大学 | Collaborative filtering recommendation method introducing video popularity and user interest change |
| CN103246672A (en) * | 2012-02-09 | 2013-08-14 | 中国科学技术大学 | Method and device for performing personalized recommendation on users |
| CN104851025A (en) * | 2015-05-09 | 2015-08-19 | 湘南学院 | Case-reasoning-based personalized recommendation method for E-commerce website commodity |
| US20160188723A1 (en) * | 2012-06-27 | 2016-06-30 | Beijing Qihoo Technology Company Limited | Cloud website recommendation method and system based on terminal access statistics, and related device |
| CN110188283A (en) * | 2019-06-05 | 2019-08-30 | 中国人民解放军国防科技大学 | Information recommendation method and system based on joint neural network collaborative filtering |
| CN110197404A (en) * | 2019-06-12 | 2019-09-03 | 合肥工业大学 | The personalized long-tail Method of Commodity Recommendation and system of popularity deviation can be reduced |
| CN110781409A (en) * | 2019-10-25 | 2020-02-11 | 东北大学秦皇岛分校 | Article recommendation method based on collaborative filtering |
| CN111143702A (en) * | 2019-12-16 | 2020-05-12 | 中国科学技术大学 | Information recommendation method, system, device and medium based on graph convolution neural network |
-
2021
- 2021-02-26 CN CN202110218946.5A patent/CN113158024B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103246672A (en) * | 2012-02-09 | 2013-08-14 | 中国科学技术大学 | Method and device for performing personalized recommendation on users |
| US20160188723A1 (en) * | 2012-06-27 | 2016-06-30 | Beijing Qihoo Technology Company Limited | Cloud website recommendation method and system based on terminal access statistics, and related device |
| CN103209342A (en) * | 2013-04-01 | 2013-07-17 | 电子科技大学 | Collaborative filtering recommendation method introducing video popularity and user interest change |
| CN104851025A (en) * | 2015-05-09 | 2015-08-19 | 湘南学院 | Case-reasoning-based personalized recommendation method for E-commerce website commodity |
| CN110188283A (en) * | 2019-06-05 | 2019-08-30 | 中国人民解放军国防科技大学 | Information recommendation method and system based on joint neural network collaborative filtering |
| CN110197404A (en) * | 2019-06-12 | 2019-09-03 | 合肥工业大学 | The personalized long-tail Method of Commodity Recommendation and system of popularity deviation can be reduced |
| CN110781409A (en) * | 2019-10-25 | 2020-02-11 | 东北大学秦皇岛分校 | Article recommendation method based on collaborative filtering |
| CN111143702A (en) * | 2019-12-16 | 2020-05-12 | 中国科学技术大学 | Information recommendation method, system, device and medium based on graph convolution neural network |
Non-Patent Citations (2)
| Title |
|---|
| KE NIU，等: "《UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty》", 《IEEE ACCESS 》 * |
| 韩勇，等: "基于社交信息和物品曝光度的矩阵分解推荐", 《浙江大学学报(工学版)》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114090890A (en) * | 2021-11-23 | 2022-02-25 | 电子科技大学 | Counterfactual project recommendation method based on graph convolution network |
| CN114090890B (en) * | 2021-11-23 | 2023-04-28 | 电子科技大学 | Method for recommending anti-facts item based on graph convolution network |
| CN114861783A (en) * | 2022-04-26 | 2022-08-05 | 北京三快在线科技有限公司 | Recommendation model training method and device, electronic equipment and storage medium |
| CN115809374A (en) * | 2023-02-13 | 2023-03-17 | 四川大学 | Method, system, device and storage medium for correcting mainstream deviation of recommendation system |
| CN117522532A (en) * | 2024-01-08 | 2024-02-06 | 浙江大学 | Popularity deviation correction recommendation method and device, electronic equipment and storage medium |
| CN117522532B (en) * | 2024-01-08 | 2024-04-16 | 浙江大学 | Popularity deviation correction recommendation method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113158024B (en) | 2022-07-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113158024B (en) | Causal reasoning method for correcting popularity deviation of recommendation system | |
| CN109087178B (en) | Commodity recommendation method and device | |
| CN111582443B (en) | Recommendation method based on Mask mechanism and level attention mechanism | |
| CN112348629A (en) | Commodity information pushing method and device | |
| CN111242729A (en) | Serialization recommendation method based on long-term and short-term interests | |
| Zheng et al. | Deviation-based contextual SLIM recommenders | |
| Navgaran et al. | Evolutionary based matrix factorization method for collaborative filtering systems | |
| CN110727872A (en) | Method and device for mining ambiguous selection behavior based on implicit feedback | |
| Salehi | An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product | |
| EP3920100A1 (en) | Adaptive pointwise-pairwise learning to rank | |
| CN113111924A (en) | Electric power customer classification method and device | |
| Le et al. | A multi-criteria collaborative filtering approach using deep learning and Dempster-Shafer theory for hotel recommendations | |
| CN113407854A (en) | Application recommendation method, device and equipment and computer readable storage medium | |
| Farahani et al. | Adaptive personalized recommender system using learning automata and items clustering | |
| CN113988951A (en) | Commodity recommendation learning model construction method based on tensor decomposition and collaborative filtering | |
| CN112991026A (en) | Commodity recommendation method, system, equipment and computer readable storage medium | |
| Wang et al. | Enhancing Long Tail Recommendation Based on User's Experience Evolution | |
| Babeetha et al. | An enhanced kernel weighted collaborative recommended system to alleviate sparsity | |
| CN116680320A (en) | Mixed matching method based on big data | |
| CN115456656A (en) | Method and device for predicting purchase intention of consumer, electronic equipment and storage medium | |
| Desrosiers et al. | Solving the sparsity problem: collaborative filtering via indirect similarities | |
| CN114219522A (en) | Customer consumption behavior prediction method and device, electronic equipment and storage medium | |
| Kuznietsova et al. | Business intelligence techniques for missing data imputation | |
| CN110956528A (en) | Recommendation method and system for e-commerce platform | |
| Sharma et al. | A review on collaborative filtering using knn algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |