CN112784171A - Movie recommendation method based on context typicality - Google Patents

Movie recommendation method based on context typicality Download PDF

Info

Publication number
CN112784171A
CN112784171A CN202110083203.1A CN202110083203A CN112784171A CN 112784171 A CN112784171 A CN 112784171A CN 202110083203 A CN202110083203 A CN 202110083203A CN 112784171 A CN112784171 A CN 112784171A
Authority
CN
China
Prior art keywords
context
user
movie
typicality
calculating
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
Application number
CN202110083203.1A
Other languages
Chinese (zh)
Other versions
CN112784171B (en
Inventor
张清华
张金镇
艾志华
李新太
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Post and Telecommunications
Original Assignee
Chongqing University of Post and Telecommunications
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN202110083203.1A priority Critical patent/CN112784171B/en
Publication of CN112784171A publication Critical patent/CN112784171A/en
Application granted granted Critical
Publication of CN112784171B publication Critical patent/CN112784171B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06F16/9536—Search customisation based on social or collaborative filtering

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention belongs to the field of computer science and technology, and particularly relates to a movie recommendation method based on context typicality, which comprises the following steps: acquiring user data, and constructing a context typicality matrix according to the user data and the context typicality of each user to different movie types; searching neighbors in the context typicality matrix, and calculating scores of neighbor users according to context attribute values of the neighbors; calculating the importance of the context attribute to each movie type by adopting knowledge granularity according to the grading information of the user data; calculating a multi-context prediction score according to the importance and the single context; recommending the movie according to the multi-context prediction scores; compared with the traditional collaborative filtering recommendation system, the method has the advantages that the traditional collaborative filtering is replaced by the typical collaborative filtering, and the sparsity of data is effectively relieved.

Description

Movie recommendation method based on context typicality
Technical Field
The invention belongs to the field of computer science and technology, and particularly relates to a movie recommendation method based on context typicality.
Background
With the increasing data of the internet, the conventional Information Retrieval (IR) has been unable to meet the user's demand. In order to be able to determine the interests of a user and to present information of interest to the user, recommendation Systems (recommendation Systems) have become the most promising way for the new era of the internet.
In the field of recommendation systems, only depending on a user-item binary relationship, the recorded data has a sparsity problem in a scoring process of the system, and the depicted preference relationship is not reasonable. Traditional collaborative filtering algorithms measure a user using item scores, capturing user preferences in the low dimension of the item scores. When data is sparse, users do not have enough common scoring items, so that similarity of the users is measured inaccurately, and information recommended to the users by the system is inaccurate.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a movie recommendation method based on context typicality, which includes:
s1: acquiring user data, and collecting the user data to obtain a data set; calculating contextual typicality of the data from information in the data; the user data comprises movie information, rating information and context attribute values;
s2: constructing a context typicality matrix according to the user data and the context typicality of each user to different movie types;
s3: searching neighbors in the context typicality matrix, and calculating scores of neighbor users according to context attribute values of the neighbors;
s4: predicting the score of the target user on the movie under the context attribute value by adopting the scores of the neighbor users;
s5: calculating the importance of the context attribute to each movie type by adopting knowledge granularity according to the grading information of the user data;
s6: calculating a multi-context prediction score according to the importance and the single context;
s7: the movie is recommended based on the multi-context prediction scores.
Preferably, the specific process of constructing the context user-typicality vector comprises:
step 1: calculating the user typicality of the user to the movie type under the context attribute value;
step 2: converting the contextual user-typicality of the user for each movie type into a contextual user-typicality vector; the expression of the context user-typicality vector is:
Figure BDA0002909862860000021
and step 3: all context user-typicality vectors are constructed as a context-typicality matrix.
Further, the formula for user-typicality of a user to movie types under the context attribute values is:
further, the context typicality matrix is:
Figure BDA0002909862860000022
preferably, the searching of the context typical matrix comprises calculating cosine similarity between the user data in the context typical matrix and the target user data, and judging the magnitude of all cosine similarity, when the user data with the largest cosine similarity is the neighbor of the context typical matrix; the cosine similarity formula is:
Figure BDA0002909862860000023
preferably, the formula for predicting scores according to the single context of the neighbor users of the target user for different movies is as follows:
Figure BDA0002909862860000024
preferably, the formula for calculating the importance of the context attribute to each movie type by using the knowledge granularity is as follows:
Figure BDA0002909862860000031
preferably, the formula of the multi-context prediction score is:
Figure BDA0002909862860000032
the invention has the advantages that:
1) compared with the traditional manual recommendation, the movie recommendation system provided by the invention has higher accuracy and efficiency;
2) compared with the traditional collaborative filtering recommendation system, the method has the advantages that the typical collaborative filtering is used for replacing the traditional collaborative filtering, so that the data sparsity is effectively relieved;
3) the preference degree of the user to the items under the context is measured by the knowledge granularity, so that the preference degree can be described more accurately;
4) and the context is introduced, the user preference is measured in the context, the method is more suitable for the actual scene, and the recommendation accuracy is improved.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a context-based exemplary matrix according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be 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 of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A movie recommendation method based on context typicality, as shown in fig. 1, the method comprising:
s1: acquiring user data, and collecting the user data to obtain a data set; calculating contextual typicality of the data from information in the data; the user data comprises movie information, rating information and context attribute values;
s2: constructing a context typicality matrix according to the user data and the context typicality of each user to different movie types;
s3: searching neighbors in the context typicality matrix, and calculating scores of neighbor users according to context attribute values of the neighbors;
s4: predicting the score of the target user on the movie under the context attribute value by adopting the scores of the neighbor users;
s5: calculating the importance of the context attribute to each movie type by adopting knowledge granularity according to the grading information of the user data;
s6: calculating a multi-context prediction score according to the importance and the single context;
s7: the movie is recommended based on the multi-context prediction scores.
After user data are obtained, the user data are collected to obtain a user data set; the user data includes movie information, rating information, and context attribute values. The data set of the user is U, UxRepresenting the xth user. The data set comprises an item set O, a context attribute set C and attribute values in the context attribute set, wherein Ch,kThe kth attribute value representing the h context attribute. In the raw data set, the historical scores of the individual users for the individual movies are also included.
After the user data is acquired, the types of the movies are classified into different types of groups T, T according to the types of the movieslIs the ith project group.
And generating a decision information table according to the classification result of the final movie, wherein the content in the decision information table is shown in table 1.
TABLE 1 decision information Table in context recommendation System
Figure BDA0002909862860000041
Figure BDA0002909862860000051
There is a lot of context information in the recommendation system, but some of them have a large influence on the decision of the user, and some have little influence on the decision. To determine which contexts are important for different types, the present invention bases on the granularity of knowledge G (d)y,t|Ch,k) To measure the importance of the context to different item types:
Figure BDA0002909862860000052
wherein d isy,tIndicating a certain film type t, C to which a film y belongsh,kA k-th attribute value representing an h-th context attribute,
Figure BDA0002909862860000053
set of scores, R, representing types of movies in the same single context attributeiRepresents one score and n represents the nth.
Context typicals are built to measure user preferences.
The specific process of constructing the context typicality matrix comprises the following steps:
(1) calculating the user typicality of the user to the movie type under the context attribute value, wherein the formula is as follows:
Figure BDA0002909862860000054
wherein
Figure BDA0002909862860000055
Indicating how well user x scores movie type t,
Figure BDA0002909862860000056
representing the frequency of scoring of movie type t by user x.
Figure BDA0002909862860000057
Wherein R isMaxIs the maximum rating of the score.
Figure BDA0002909862860000058
Wherein,
Figure BDA0002909862860000059
indicates the number of times user x scored the movie type under the context attribute value, indicates
Figure BDA00029098628600000510
The number of times user x scores all movie types under the context attribute value.
(2) Converting the contextual user-typicality of the user for each movie type into a contextual user-typicality vector; the expression of the context user-typicality vector is:
Figure BDA0002909862860000061
a context-typicality matrix is then constructed from the context-user-typicality vectors.
As shown in fig. 2, the expression of the context typicality matrix is:
Figure BDA0002909862860000062
wherein m represents the number of users, n represents the number of user groups,
Figure BDA0002909862860000063
representing user uxContext attribute value Ch,kThe user representative vector of (a).
Searching adjacent cosine similarity between user data in the context typical matrix and target user data, and judging the cosine similarity, wherein the adjacent cosine similarity comprises the cosine similarity between the user data in the context typical matrix and the target user data; the cosine similarity formula is:
Figure BDA0002909862860000064
where | l | · | | represents the modulus of the vector,
Figure BDA0002909862860000065
a context representative vector of the representation.
After the similarity between the target user and other users is calculated, a prediction score is obtained based on the other users 'context typicality of the movie and the project's score in the current context. The formula is as follows:
Figure BDA0002909862860000066
wherein d isy,tRepresents the movie type t, C to which movie y belongsh,kA k-th attribute value representing an h-th context attribute,
Figure BDA0002909862860000067
set of scores, R, representing types of movies in the same single context attributex,yRepresents the rating of movie y by user x, n represents the nth rating,
Figure BDA0002909862860000068
indicating a neighbor user i context attribute value Ch,kThe history of movie y is scored.
For target user u in multi-context cxThe score prediction for the ith item is calculated as:
Figure BDA0002909862860000071
where x represents the x-th user, C represents the set of context attributes where the user is located, y represents the y-th movie, Ch,kK-th attribute value, d, representing h-th context attributey,tIndicates the movie type, G (d), to which movie y belongsy,t|Ch,k) Represents a context attribute value Ch,kFor type d of filmy,tThe importance of (a) to (b),
Figure BDA0002909862860000072
representing the single context prediction score of user x for movie y.
The formula for calculating the importance of the context attribute to each movie type by using the knowledge granularity formula is as follows:
Figure BDA0002909862860000073
wherein d isy,tRepresents the movie type t, C to which movie y belongsh,kA k-th attribute value representing an h-th context attribute,
Figure BDA0002909862860000074
set of scores, R, representing types of movies in the same single context attributex,yRepresenting the rating of movie y by user x and n representing the nth rating.
The formula for the multi-context prediction score is:
Figure BDA0002909862860000075
where x represents the x-th user, C represents the set of context attributes where the user is located, y represents the y-th movie, Ch,kK-th attribute value, d, representing h-th context attributey,tRepresents the movie type t, G (d) to which movie y belongsy,t|Ch,k) Represents a context attribute value Ch,kFor type d of filmy,tThe importance of (a) to (b),
Figure BDA0002909862860000076
representing the single context prediction score of user x for movie y.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for recommending movies based on contextual representativeness, the method comprising:
s1: acquiring user data, and collecting the user data to obtain a data set; calculating contextual typicality of the data from information in the data; the user data comprises movie information, rating information and context attribute values;
s2: constructing a context typicality matrix according to the user data and the context typicality of each user to different movie types;
s3: searching neighbors in the context typicality matrix, and calculating scores of neighbor users according to context attribute values of the neighbors;
s4: predicting the score of the target user on the movie under the context attribute value by adopting the scores of the neighbor users;
s5: calculating the importance of the context attribute to each movie type by adopting knowledge granularity according to the grading information of the user data;
s6: calculating a multi-context prediction score according to the importance and the single context;
s7: the movie is recommended based on the multi-context prediction scores.
2. The method of claim 1, wherein the specific process of constructing the context-user-typicality vector comprises:
step 1: calculating the user typicality of the user to the movie type under the context attribute value;
step 2: converting the contextual user-typicality of the user for each movie type into a contextual user-typicality vector; the expression of the context user-typicality vector is:
Figure FDA0002909862850000011
and step 3: constructing all context user representativeness vectors into a context representativeness matrix;
wherein,
Figure FDA0002909862850000012
representing a contextual user-typicality vector, Ch,kA k-th attribute value representing an h-th context attribute,
Figure FDA0002909862850000013
representing user x in context attribute value Ch,kUser typicality for the nth movie type.
3. The method of claim 2, wherein the formula for calculating the user-typicality of the user to the movie type under the context attribute value is:
Figure FDA0002909862850000021
wherein,
Figure FDA0002909862850000022
indicates the degree of rating, C, of user x for movie type th,kThe kth attribute value, which represents the h context attribute, r represents the degree of scoring,
Figure FDA0002909862850000023
represents the frequency of scoring of movie type t by user x, and f represents the scoreFrequency.
4. The method of claim 2, wherein the context representativeness matrix is:
Figure FDA0002909862850000024
wherein m represents the number of users, n represents the number of user groups,
Figure FDA0002909862850000025
value C of a contextual attribute representing user xh,kThe user representative vector of (a).
5. The method according to claim 1, wherein the step of finding neighbors in the context canonical matrix comprises calculating cosine similarities between the user data in the context canonical matrix and the target user data, and determining the magnitude of all cosine similarities, wherein when the user data with the largest cosine similarity is a neighbor of the context canonical matrix; the cosine similarity formula is:
Figure FDA0002909862850000026
where | l | · | | represents the modulus of the vector,
Figure FDA0002909862850000027
a context representative vector of the representation.
6. The method of claim 1, wherein the formula for the prediction score of the neighbor user for a single context of different types of movies is:
Figure FDA0002909862850000028
wherein
Figure FDA0002909862850000029
Represents a context attribute value Ch,kUser-typicality of next user i to type t to which movie y belongs,
Figure FDA0002909862850000031
indicating a neighbor user i context attribute value Ch,kThe history of movie y is scored.
7. A method as claimed in claim 1, wherein the formula for calculating the importance of the context attribute to each movie type by using the knowledge granularity is as follows:
Figure FDA0002909862850000032
wherein d isy,tRepresents the movie type t, C to which movie y belongsh,kA k-th attribute value representing an h-th context attribute,
Figure FDA0002909862850000033
set of scores, R, representing types of movies in the same single context attributex,yRepresenting the rating of movie y by user x and n representing the nth rating.
8. The method of claim 1, wherein the multi-context prediction score is formulated as:
Figure FDA0002909862850000034
wherein x represents the xthUser, C represents the set of context attributes in which the user is located, y represents the y-th movie, Ch,kK-th attribute value, d, representing h-th context attributey,tRepresents the movie type t, G (d) to which movie y belongsy,t|Ch,k) Represents a context attribute value Ch,kFor type d of filmy,tThe importance of (a) to (b),
Figure FDA0002909862850000035
representing the single context prediction score of user x for movie y.
CN202110083203.1A 2021-01-21 2021-01-21 Movie recommendation method based on context typicality Active CN112784171B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110083203.1A CN112784171B (en) 2021-01-21 2021-01-21 Movie recommendation method based on context typicality

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110083203.1A CN112784171B (en) 2021-01-21 2021-01-21 Movie recommendation method based on context typicality

Publications (2)

Publication Number Publication Date
CN112784171A true CN112784171A (en) 2021-05-11
CN112784171B CN112784171B (en) 2022-11-04

Family

ID=75758271

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110083203.1A Active CN112784171B (en) 2021-01-21 2021-01-21 Movie recommendation method based on context typicality

Country Status (1)

Country Link
CN (1) CN112784171B (en)

Citations (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030126606A1 (en) * 2001-12-27 2003-07-03 Koninklijke Philips Esectronics N.V. Hierarchical decision fusion of recommender scores
US20040076936A1 (en) * 2000-07-31 2004-04-22 Horvitz Eric J. Methods and apparatus for predicting and selectively collecting preferences based on personality diagnosis
CN102508846A (en) * 2011-09-26 2012-06-20 深圳中兴网信科技有限公司 Internet based method and system for recommending media courseware
CN102915307A (en) * 2011-08-02 2013-02-06 腾讯科技（深圳）有限公司 Device and method for recommending personalized information and information processing system
CN103593410A (en) * 2013-10-22 2014-02-19 上海交通大学 System for search recommendation by means of replacing conceptual terms
CN104008193A (en) * 2014-06-12 2014-08-27 安徽融数信息科技有限责任公司 Information recommending method based on typical user group finding technique
CN104881499A (en) * 2015-06-19 2015-09-02 中国石油大学(华东) Collaborative filtering recommendation method based on attribute rating scaling
CN104899321A (en) * 2015-06-19 2015-09-09 中国石油大学(华东) Collaborative filtering recommendation method based on item attribute score mean value
CN105868334A (en) * 2016-03-28 2016-08-17 云南财经大学 Personalized film recommendation method and system based on feature augmentation
CN106295675A (en) * 2016-07-25 2017-01-04 南京邮电大学 A kind of Activity recognition method of based on smart mobile phone of high accuracy
CN106326483A (en) * 2016-08-31 2017-01-11 华南理工大学 Collaborative recommendation method with user context information aggregation
CN106484876A (en) * 2016-10-13 2017-03-08 中山大学 A kind of based on typical degree and the collaborative filtering recommending method of trust network
CN106802956A (en) * 2017-01-19 2017-06-06 山东大学 A kind of film based on weighting Heterogeneous Information network recommends method
CN106991122A (en) * 2017-02-27 2017-07-28 四川大学 A kind of film based on particle cluster algorithm recommends method
CN107909498A (en) * 2017-10-26 2018-04-13 厦门理工学院 Based on the recommendation method for maximizing receiver operating characteristic curve area under
CN108256093A (en) * 2018-01-29 2018-07-06 华南理工大学 A kind of Collaborative Filtering Recommendation Algorithm based on the more interest of user and interests change
CN108268259A (en) * 2016-12-30 2018-07-10 谷歌有限责任公司 It is dynamically generating customized to apply getting started tutorial
CN108804683A (en) * 2018-06-13 2018-11-13 重庆理工大学 Associate(d) matrix decomposes and the film of collaborative filtering recommends method
CN109241412A (en) * 2018-08-17 2019-01-18 深圳先进技术研究院 A kind of recommended method, system and electronic equipment based on network representation study
CN111723302A (en) * 2020-06-09 2020-09-29 云南大学 Recommendation method based on collaborative dual-model deep representation learning

Patent Citations (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040076936A1 (en) * 2000-07-31 2004-04-22 Horvitz Eric J. Methods and apparatus for predicting and selectively collecting preferences based on personality diagnosis
US20030126606A1 (en) * 2001-12-27 2003-07-03 Koninklijke Philips Esectronics N.V. Hierarchical decision fusion of recommender scores
CN102915307A (en) * 2011-08-02 2013-02-06 腾讯科技（深圳）有限公司 Device and method for recommending personalized information and information processing system
CN102508846A (en) * 2011-09-26 2012-06-20 深圳中兴网信科技有限公司 Internet based method and system for recommending media courseware
CN103593410A (en) * 2013-10-22 2014-02-19 上海交通大学 System for search recommendation by means of replacing conceptual terms
CN104008193A (en) * 2014-06-12 2014-08-27 安徽融数信息科技有限责任公司 Information recommending method based on typical user group finding technique
CN104881499A (en) * 2015-06-19 2015-09-02 中国石油大学(华东) Collaborative filtering recommendation method based on attribute rating scaling
CN104899321A (en) * 2015-06-19 2015-09-09 中国石油大学(华东) Collaborative filtering recommendation method based on item attribute score mean value
CN105868334A (en) * 2016-03-28 2016-08-17 云南财经大学 Personalized film recommendation method and system based on feature augmentation
CN106295675A (en) * 2016-07-25 2017-01-04 南京邮电大学 A kind of Activity recognition method of based on smart mobile phone of high accuracy
CN106326483A (en) * 2016-08-31 2017-01-11 华南理工大学 Collaborative recommendation method with user context information aggregation
CN106484876A (en) * 2016-10-13 2017-03-08 中山大学 A kind of based on typical degree and the collaborative filtering recommending method of trust network
CN108268259A (en) * 2016-12-30 2018-07-10 谷歌有限责任公司 It is dynamically generating customized to apply getting started tutorial
CN106802956A (en) * 2017-01-19 2017-06-06 山东大学 A kind of film based on weighting Heterogeneous Information network recommends method
CN106991122A (en) * 2017-02-27 2017-07-28 四川大学 A kind of film based on particle cluster algorithm recommends method
CN107909498A (en) * 2017-10-26 2018-04-13 厦门理工学院 Based on the recommendation method for maximizing receiver operating characteristic curve area under
CN108256093A (en) * 2018-01-29 2018-07-06 华南理工大学 A kind of Collaborative Filtering Recommendation Algorithm based on the more interest of user and interests change
CN108804683A (en) * 2018-06-13 2018-11-13 重庆理工大学 Associate(d) matrix decomposes and the film of collaborative filtering recommends method
CN109241412A (en) * 2018-08-17 2019-01-18 深圳先进技术研究院 A kind of recommended method, system and electronic equipment based on network representation study
CN111723302A (en) * 2020-06-09 2020-09-29 云南大学 Recommendation method based on collaborative dual-model deep representation learning

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ADOMAVICIUS G: "Toward the next generation of recommend systems s survey of the state-of-the-art and possible extensions", 《IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING》 *
王妍等: "VOD电影推荐算法比较研究", 《现代电影技术》 *
钟足峰等: "可提高多样性的基于重排序图书推荐算法研究", 《现代情报》 *

Also Published As

Publication number Publication date
CN112784171B (en) 2022-11-04

Similar Documents

Publication Publication Date Title
Wu et al. A novel clustering method for static video summarization
CN106095893B (en) A kind of cross-media retrieval method
CN106021362B (en) Generation, image searching method and the device that the picture feature of query formulation represents
CN103544216B (en) The information recommendation method and system of a kind of combination picture material and keyword
CN105138653B (en) It is a kind of that method and its recommendation apparatus are recommended based on typical degree and the topic of difficulty
WO2016180270A1 (en) Webpage classification method and apparatus, calculation device and machine readable storage medium
JP2010504593A (en) Extracting dominant colors from an image using a classification technique
CN110032679B (en) Dynamic news recommendation method based on hierarchical attention network
CN111460961B (en) Static video abstraction method for CDVS-based similarity graph clustering
CN113807422B (en) Weighted graph convolutional neural network scoring prediction model integrating multi-feature information
CN110688549B (en) Artificial intelligence classification method and system based on knowledge system map construction
CN107368540A (en) The film that multi-model based on user's self-similarity is combined recommends method
CN109977299B (en) Recommendation algorithm fusing project popularity and expert coefficient
CN111460251A (en) Data content personalized push cold start method, device, equipment and storage medium
CN116541607B (en) Intelligent recommendation method based on commodity retrieval data analysis
CN109816015B (en) Recommendation method and system based on material data
CN113434778B (en) Recommendation method based on regularization framework and attention mechanism
CN111859004A (en) Retrieval image acquisition method, device, equipment and readable storage medium
CN108874916A (en) A kind of stacked combination collaborative filtering recommending method
CN109086830B (en) Typical correlation analysis near-duplicate video detection method based on sample punishment
Lumauag et al. An enhanced recommendation algorithm based on modified user-based collaborative filtering
CN112380451A (en) Favorite content recommendation method based on big data
CN115712780A (en) Information pushing method and device based on cloud computing and big data
CN118193778B (en) Multi-feature-fused remote sensing image retrieval method
JP5592337B2 (en) Content conversion method, content conversion apparatus, and content conversion program

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