CN103632290A - Recommendation probability fusion based hybrid recommendation method - Google Patents
Recommendation probability fusion based hybrid recommendation method Download PDFInfo
- Publication number
- CN103632290A CN103632290A CN201310637512.4A CN201310637512A CN103632290A CN 103632290 A CN103632290 A CN 103632290A CN 201310637512 A CN201310637512 A CN 201310637512A CN 103632290 A CN103632290 A CN 103632290A
- Authority
- CN
- China
- Prior art keywords
- user
- scoring
- commodity
- outcome
- represent
- 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
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a recommendation probability fusion based hybrid recommendation method. The method includes the following steps: (1) using a two-dimensional table for representing scoring data of commodities; (2) taking any items in a scored set as unknowns, utilizing a basic recommendation method to obtain a prediction result of each corresponding item, training sets of scores of the scored items and the forecast results of the corresponding items by the aid of a neural network so as to obtain an SFM (score forecast model); (3) utilizing the basic recommendation method to obtain non-scored item forecast results, and utilizing the SFM to obtain a final forecast value of each non-scored item of a set of the non-scored item forecast results; (4) sorting all the non-scored items of a user according to size of each forecast value in a final forecast value set in a descending manner to obtain a non-scored item sorted set, and selecting top N items of the non-scored item sorted set as recommendation results to be recommended to the user. By the method, true situation of user evaluation can be effectively reflected, and precision in personalized recommendation is improved.
Description
Technical field
The invention belongs to e-commerce field, specifically a kind of mixing recommend method based on recommending probability fusion.
Background technology
Along with the fast development of ecommerce, information overload phenomenon is further serious.The individual demand how the commodity set based on magnanimity meets user becomes and promotes the major issue that user experiences, improves user satisfaction.Personalized recommendation system is the important means that meets users ' individualized requirement.Personalized recommendation system builds user interest preference model according to the online browse data of user's individuality or purchase data, thereby to user, recommends to meet the commodity of its unique need.Personalized commercial is recommended in the e-commerce websites such as Amazon, store, Jingdone district, Taobao and is widely applied, and has effectively improved the possibility that user buys, and has promoted the experience of user to website service.
Collaborative filtering (Collaborative Filtering) technology is the earliest and the most successfully one of technology of personalized recommendation application, its main thought be its interest preference of user based on thering is similar features also identical this hypothesis build user interest preference model.Although existing research method can be provided fundamental basis and practical advice for the structure of personalized recommendation system, still has many defects:
(1) what recommendation information represented is imperfect.Existing recommend method conventionally represents user or is predicted as a concrete numerical value to the evaluation of commodity, as predictive user thinks that to being evaluated as of commodity scoring is that the possibility of 3 minutes is 100% to user to commodity for 3 minutes.In fact, user to the evaluation of particular commodity conventionally in a kind of uncertain state, as the evaluation to a certain commodity is generally " well ", " all right ", " pretty good ".User is expressed as to user to the nondeterministic statement of commodity evaluation and for commodity, provides the possibility of different scorings, for example commodity are evaluated as to the possibility of 3 minutes, 4 minutes, 5 minutes and are respectively 30%, 40% and 20%, can effectively reflect the truth that user evaluates, to improving the precision of personalized recommendation, there is active influence.Existing method is shown as a concrete numerical value by user's grade form and has ignored the uncertainty that user evaluates, and cannot reflect that user evaluates the truth of commodity, has reduced the precision of commending system.
(2) the fusion problem of recommendation information.The article that collaborative filtering method based on user recommends those and him to have the user of common interest hobby to like by calculating neighbor user, recommendation results focuses on the focus of the microcommunity that reflection is similar with user interest; Project-based collaborative filtering method is recommended those and the similar commodity of commodity that he selected in the past by calculating commodity neighbours to user, and recommendation results focuses on the historical interest of maintaining user.Collaborative filtering based on user, project-based collaborative filtering method produce personalized recommendation result from user and project angle respectively, and the above results is merged and can the recommendation information of various angles be fully utilized, and improve the precision of personalized recommendation.In existing research, lack the Unified frame that different angles recommendation information is merged.For example, a kind of Collaborative Filtering Recommendation Algorithm based on Collaborative Filtering utilizes predicting the outcome as the input of the collaborative filtering method based on user that project-based collaborative filtering method obtains, although fully utilized collaborative filtering method and project-based collaborative filtering method based on user, it does not merge the collaborative filtering result based on user and project-based collaborative filtering result.
Summary of the invention
The present invention overcomes the weak point that prior art exists, a kind of mixing recommend method based on recommending probability fusion is proposed, not only for merging the recommendation results of different recommend methods generations, provide unified framework, and can effectively reflect the truth that user evaluates, improve the precision of personalized recommendation.
In order to achieve the above object, the technical solution adopted in the present invention is:
The present invention is a kind of is to carry out as follows based on recommending the feature of the mixing recommend method of probability fusion:
In described bivariate table T, U={U
1..., U
u..., U
| U|represent that user gathers, I={I
1..., I
i..., I
| I|representing commodity set, f represents the scoring of user to commodity;
Described user gathers in U, | total number that U| is user, U
urepresent u user; In described commodity set I, | total number that I| is commodity, I
irepresent i commodity; Suppose user U
uto commodity I
igrading system S be { S
1..., S
s..., S
| S|, in described grading system S, scoring S
sfor integer and S
1< ... < S
s< ... < S
| S|, S
1represent the minimum scoring of commodity, S
| S|represent the highest scoring of commodity;
In described bivariate table T, by existing scoring set of marking for item of all users
represent,
for described user U
uthe set of scoring,
represent user U
uthe existing scoring of i,
represent user U
utotal number of existing scoring; Order
for described user U
unot scoring set,
represent user U
uscoring of i,
represent user U
utotal number of not scoring, make the described user set of not marking
in Arbitrary Term
By the described item set that predicts the outcome of having marked
as the input value of neural network, by the described user set of having marked
as the output valve of described neural network, described neural network is trained, obtain score in predicting model SFM;
The feature that the present invention is based on the mixing recommend method of recommending probability fusion is also:
Collaborative filtering method based on user in described step 2 is to carry out as follows:
1) by described user U
uto the user of all commodity set of having marked
to the set of scoring of all commodity, utilize respectively constrained Pearson came relatedness metric method to obtain user U with other users
uwith user's similarity set of other users, other users are carried out to descending sort according to the similarity size in the set of described user's similarity and obtain preliminary neighbor user set N
u;
2) by described commodity I
iin the set of marking
in corresponding subscriber's meter be shown scoring user gather R
i;
3) by described preliminary neighborhood N
ugather R with scoring user
ifront k the subscriber's meter occuring simultaneously is shown neighbor user set N
ui;
4) by described neighbor user set N
uiin each user to commodity I
igrade form be shown neighbor user scoring set F
ui;
In formula (1), Num
u(S
s) be described neighbor user scoring set F
uimiddle scoring S
sthe number of times occurring;
6) by described scoring S
swith the described scoring probability based on user
forming neighbor user predicts the outcome
In formula (2),
Described project-based collaborative filtering method is to carry out as follows:
1) by all users to described commodity I
imark set and to the set of scoring of other commodity, utilize respectively constrained Pearson came relatedness metric method to obtain described commodity I
iwith the commodity similarity set of other commodity, other commodity are carried out to descending sort according to the similarity size in the set of described commodity similarity and obtain preliminary neighbours' commodity set N
i;
2) by user U
uin the set of marking
in corresponding commodity list be shown scoring commodity set R
u;
3) by described preliminary neighbours' commodity set N
iwith scoring commodity set R
ufront k the commodity list occuring simultaneously is shown neighbours' commodity set N
iu;
4) by user U
uto described neighbours' commodity set N
iuin the grade form of each commodity be shown neighbours' commodity scoring set F
iu;
In formula (3), Num
i(S
s) be described neighbours' commodity scoring set F
iumiddle scoring S
sthe number of times occurring;
6) by described scoring S
swith described project-based scoring probability
formation project neighbours predict the outcome
In formula (4),
Compared with the prior art, beneficial effect of the present invention is embodied in:
1, the present invention is expressed as recommendation probability by the recommendation results that obtained separately based on user and project-based recommend method, utilize neural network that the recommendation results that obtained is separately merged, overcome conventional hybrid recommend method recommendation information and represented incomplete problem, for the fusion of different angles recommendation information provides unified framework, it recommends precision to be obviously better than recommend method and the project-based recommend method based on user.
2, the present invention utilizes scoring S
stwo tuple-sets that form with scoring probability based on user
represent the recommendation information that the recommend method based on user obtains, utilize scoring S
stwo tuple-sets that form with project-based scoring probability
represent the recommendation information that project-based recommend method obtains, and user is shown to a concrete numerical value to the evaluation table of commodity compares, two tuples that scoring and scoring probability form can reflect the truth that user evaluates more really.
3, the collaborative filtering method of utilization of the present invention based on user and project-based collaborative filtering method obtain marking and predict the outcome, the item of marking is predicted the outcome to set as input, using the set of marking as output, utilize the distinctive capability of fitting of neural network to obtain score in predicting model SFM, guaranteed the robustness of score in predicting model.
4, the present invention utilizes collaborative filtering method and project-based collaborative filtering method based on user to obtain tentative prediction result the set of not marking, input using tentative prediction result as a score in predicting model SFM final predicted value set that obtains not marking, and then obtain final recommendation results, compare with traditional recommend method, the present invention can carry out effective integration to the recommendation results of different recommend methods, has improved the precision of personalized recommendation.
5, the present invention carries mixing recommend method the result of the collaborative filtering method based on user and project-based collaborative filtering method has been carried out merging the diversity that is conducive to improve recommendation results, the actual preferences that more meets user, has overcome the shortcoming of using merely the collaborative filtering method based on user in prior art or using merely project-based collaborative filtering method.
6, the present invention can be used for the personalized recommendation system of digital product, the travelling routes such as the entity products such as clothes and mobile phone, film and music and the service products such as arrangement of spending a holiday, can use at platforms such as the webpage of computer and mobile phone and App, have wide range of applications.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention;
Fig. 2 is the sensitivity experiments result of method for measuring similarity;
Fig. 3 is neighbor user (commodity) number sensitivity experiments result;
Fig. 4 is forecasting accuracy experimental result;
Fig. 5 is prediction training pattern sensitivity experiments result.
Embodiment
The present invention utilizes bivariate table to represent the score data of commodity, to mark set in Arbitrary Term as unknown number, utilize basic recommend method to obtain predicting the outcome of respective items, utilize neural network to train the set predicting the outcome of mark item rating and respective items and obtain score in predicting model SFM.The set that the not scoring item obtaining by basic recommend method is predicted the outcome utilizes score in predicting model SFM to obtain the not final predicted value of scoring item.Finally, on standard data set, compare with basic algorithm.As shown in Figure 1, the method for the embodiment of the present invention comprises the following steps:
As table 1, U={U
1..., U
u..., U
| U|represent that user gathers, I={I
1..., I
i..., I
| I|representing commodity set, f represents the scoring of user to commodity;
User gathers in U, | total number that U| is user, U
urepresent u user; In commodity set I, | total number that I| is commodity, I
irepresent i commodity; Suppose user U
uto commodity I
igrading system S be { S
1..., S
s..., S
| S|, in grading system S, scoring S
sfor integer and S
1< ... < S
s< ... < S
| S|, S
1represent the minimum scoring of commodity, S
| S|represent the highest scoring of commodity;
Table 1
In bivariate table T, by existing scoring set of marking for item of all users
represent,
for the set of scoring of user Uu,
represent user U
uthe existing scoring of i,
represent user U
utotal number of existing scoring; Order
for user U
unot scoring set,
represent user U
uscoring of i,
represent user U
utotal number of not scoring, make user's set of not marking
in Arbitrary Term
1) suppose user's set of having marked
in Arbitrary Term
for unknown number, by user's set of having marked
in the collaborative filtering method of other utilizations based on the user neighbor user that obtains Arbitrary Term predict the outcome
other fingers are in the set of marking
In, except
all items in addition;
1.1) by user U
uto all commodity I={I
1..., I
i..., I
| I|user's set of having marked
to the set of scoring of all commodity, utilize respectively constrained Pearson came relatedness metric method to obtain user U with other users
uwith user's similarity set of other users, other users are carried out to descending sort according to the similarity size in the set of user's similarity and obtain preliminary neighbor user set N
u, other users refer to user and gather U={U
1..., U
u..., U
| U|in except user U
uall users in addition, other users refer in the set of marking the set of scoring of all commodity
in except user U
uthe set of scoring
the set of marking of all users in addition; User's method for measuring similarity comprises three kinds of cosine similarity, Pearson came correlativity and constrained Pearson came correlativitys, for more different method for measuring similarity the present invention is directed to and designed 5 groups of experiments for the impact of the inventive method precision of prediction, experimental result as shown in Figure 2.In figure, horizontal ordinate represents data set, ordinate represents to test predicated error, in figure curve we can find out, for the standard data set in the present invention, the error of the method for measuring similarity based on constrained Pearson came correlativity is all lower than cosine method for measuring similarity and Pearson's relativity measurement method, thereby contributes to improve the precision of prediction of the inventive method.
1.2) by commodity I
iin the set of marking
in corresponding subscriber's meter be shown scoring user gather R
i;
1.3) by preliminary neighbor user set N
ugather R with scoring user
ifront k the subscriber's meter occuring simultaneously is shown neighbor user set N
ui; The selection of k is the key factor of the collaborative filtering recommending method effect of impact based on user, and in order to verify the impact of neighbor user number on the inventive method precision of prediction, the present invention has designed 7 groups of experiments, k neighbor user, k=10,20 are chosen respectively in every group of experiment,, 80; And calculate precision of prediction.As shown in Figure 3, in figure, horizontal ordinate represents neighbor user number to experimental result, and ordinate represents to test predicated error.When neighbor user, count k hour, prediction probability is accurate not, has reduced the accuracy of prediction; When neighbor user, count k when larger, the similarity between user is not high, also can reduce the accuracy of algorithm predicts.Therefore,, for standard data set that this experiment adopts, when being chosen between [30,70], neighbor user number can obtain good prediction effect; For other data sets, best neighbor number of users definite depends on concrete data cases.
1.4) by neighbor user set N
uiin each user to commodity I
igrade form be shown neighbor user scoring set F
ui;
1.5) utilize formula (1) to obtain the scoring probability based on user
, the user U that uses the collaborative filtering method based on user to obtain
uto commodity I
igrading system be S
sprobability:
In formula (1), Num
u(S
s) be described neighbor user scoring set F
uimiddle scoring S
sthe number of times occurring;
1.6) S that will mark
swith the scoring probability based on user
forming neighbor user predicts the outcome
In formula (2),
2) suppose user's set of having marked
in Arbitrary Term
for unknown number, by user's set of having marked
in other project neighbours that utilize project-based collaborative filtering method to obtain Arbitrary Term predict the outcome
, other fingers are in the set of marking
in, except i existing scoring
all items in addition;
2.1) user is gathered to U to commodity I
ithe set and utilize respectively constrained Pearson came relatedness metric method to obtain commodity I to the set of scoring of other commodity of marking
iwith the commodity similarity set of other commodity, other commodity are carried out to descending sort according to the similarity size in the set of commodity similarity and obtain preliminary commodity neighborhood N
i; Other commodity refer at commodity set I={I
1..., I
i..., I
| I|in except commodity I
iall commodity in addition.
2.2) by user U
uin the set of marking
in corresponding commodity list be shown scoring commodity set R
u;
2.3) by preliminary commodity neighborhood N
iwith scoring commodity set R
ufront k the commodity list occuring simultaneously is shown commodity neighborhood N
iu; The selection of k is the key factor of the project-based collaborative filtering recommending method effect of impact, and in order to verify the impact of neighbours' item number on the inventive method precision of prediction, the present invention has designed 7 groups of experiments, k neighbours' project, k=10,20 are chosen respectively in every group of experiment,, 80; And calculate precision of prediction.As shown in Figure 3, in figure, horizontal ordinate represents neighbours' item number to experimental result, and ordinate represents to test predicated error.When neighbours' item number k hour, prediction probability is accurate not, has reduced the accuracy of prediction; When neighbours' item number k is larger, the similarity between project is not high, also can reduce the accuracy of algorithm predicts.Therefore,, for standard data set that this experiment adopts, when being chosen between [30,70], neighbor user number can obtain good prediction effect; For other data sets, best neighbor number of users definite depends on concrete data cases.
2.4) by user U
uto commodity neighborhood N
iuin the grade form of each commodity be shown neighbours' commodity scoring set F
iu;
2.5) utilize formula (3) to obtain project-based scoring probability
, the user U obtaining with project-based collaborative filtering method
uto commodity I
igrading system be S
sprobability:
In formula (3), Num
i(S
s) be neighbours' commodity scoring set F
iumiddle scoring S
sthe number of times occurring;
2.6) S that will mark
swith project-based scoring probability
formation project neighbours predict the outcome
3) neighbor user is predicted the outcome
with neighbours' project forecast result
as Arbitrary Term
the item of scoring predict the outcome
particularly, the item of scoring of Arbitrary Term predict the outcome into:
To all execution steps 1 in the set of marking) and step 2), by the set of marking
in all set that predict the outcome with marking that predict the outcome
represent;
4) by the item set that predicts the outcome of marking
as the input value of neural network, by the set of marking
as the output valve of neural network, neural network is trained, obtain score in predicting model SFM.In the present embodiment, the neural network adopting refers to radial base neural net.Radial base neural net has stronger input and output mapping function, has the characteristic that unique the best is approached, and learning process fast convergence rate.
For the inventive method, carry out experimental demonstration, specifically comprise:
1) prepare standard data set
The present invention uses MovieLens data set as the validity of standard data set checking probability fusion recommend method, and MovieLens data set is widely used personalized recommendation data set.In MovieLens data centralization, the film that user has seen oneself is marked, and score value is 1 to 5 minute, and data set comprises 943 isolated users, 1682 films, 100000 scorings.The rule of training set and test set employing 80%/20% is cut apart, and selects at random 80000 scorings as training set, and 20000 scorings are as test set.
2) evaluation index
Adopt mean absolute error (MAE) as the evaluation index of the present embodiment.Mean absolute deviation MAE is by calculating the accuracy of user's scoring and the deviation measurement prediction between the final predicted value of respective items actual in test set, and MAE is less, recommends quality higher.If actual user marks, set is { p
1..., p
l..., p
n, corresponding predicted value set expression is { q
1..., q
l..., q
n, mean absolute error is defined as formula (5):
3) on standard data set, test
In order to verify the validity of institute of the present invention extracting method, herein on 5 group data sets of MovieLens data set, carry out model and forecast, and will predict the outcome and compare with true scoring.As shown in Figure 4, in figure, horizontal ordinate represents data set sequence number to experimental result, and ordinate represents to test predicated error.Compare with the collaborative filtering method based on product with the collaborative filtering method based on user, the predicated error of method of the present invention is all lower than the method based on user and project-based method, thereby all can obtain more excellent predictablity rate on each data set.
Robustness for checking institute of the present invention extracting method, by changing hidden layer neuron quantity and the hidden layer learning function of radial base neural net, has designed respectively 7 groups and has verified herein.As shown in Figure 5, in figure, horizontal ordinate represents to test sequence number to experimental result, and ordinate represents to test predicated error.As seen from Figure 5, change hidden layer neuron quantity and the hidden layer learning function of radial base neural net, the predicated error of the inventive method there will be certain variation; But, under reasonably hidden layer neuron quantity and hidden layer learning function arrange, the predicated error of this paper method is all the time lower than the method based on user and project-based method, thereby the present invention is better than collaborative filtering method and the collaborative filtering recommending method based on product based on user.
Claims (3)
1. the mixing recommend method based on recommending probability fusion, is characterized in that carrying out as follows:
Step 1, use bivariate table T={U, I, f} represents the score data of commodity;
In described bivariate table T, U={U
1..., U
u..., U
| U|represent that user gathers, I={I
1..., I
i..., I
| I|representing commodity set, f represents the scoring of user to commodity;
Described user gathers in U, | total number that U| is user, U
urepresent u user; In described commodity set I, | total number that I| is commodity, I
irepresent i commodity; Suppose user U
uto commodity I
igrading system S be { S
1..., S
s..., S
| S|, in described grading system S, scoring S
sfor integer and S
1< ... < S
s< ... < S
| S|, S
1represent the minimum scoring of commodity, S
| S|represent the highest scoring of commodity;
In described bivariate table T, by existing scoring set of marking for item of all users
represent,
for described user U
uthe set of scoring,
represent user U
uthe existing scoring of i,
represent user U
utotal number of existing scoring; Order
for described user U
unot scoring set,
represent user U
uscoring of i,
represent user U
utotal number of not scoring, make the described user set of not marking
in Arbitrary Term
Step 2, suppose user's set of having marked
in Arbitrary Term
for unknown number, by user's set of having marked
in collaborative filtering method and the project-based collaborative filtering method of other utilizations based on user obtain respectively described Arbitrary Term
neighbor user predict the outcome
with neighbours' project forecast result
described neighbor user is predicted the outcome
with neighbours' project forecast result
as described Arbitrary Term
the item of scoring predict the outcome
by user's set of having marked
in the set that predicts the outcome with marking that predicts the outcome of all items of scoring
represent;
By the described item set that predicts the outcome of having marked
as the input value of neural network, by the described user set of having marked
as the output valve of described neural network, described neural network is trained, obtain score in predicting model SFM;
Step 3, by user's set of having marked
in all described collaborative filtering method based on user and project-based collaborative filtering methods of utilizing obtain the described user set of not marking
in Arbitrary Term
tentative prediction result
by the described user set of not marking
in all predicting the outcome with the set that predicts the outcome of scoring not
represent; By the set that predicts the outcome of described scoring item
as the input value of described score in predicting model SFM, utilize described score in predicting model SFM to obtain a not scoring final predicted value set;
Step 4, by described user U
uall scorings according to the size of each predicted value in a described scoring final predicted value set, carry out a descending sort acquisition ordered set of not marking, described in choosing, the front N item of a scoring ordered set is not recommended described user U as recommendation results
u.
2. the mixing recommend method based on recommending probability fusion according to claim 1, is characterized in that: the collaborative filtering method based on user in described step 2 is to carry out as follows:
1) by described user U
uto the user of all commodity set of having marked
to the set of scoring of all commodity, utilize respectively constrained Pearson came relatedness metric method to obtain user U with other users
uwith user's similarity set of other users, other users are carried out to descending sort according to the similarity size in the set of described user's similarity and obtain preliminary neighbor user set N
u;
2) by described commodity I
iin the set of marking
in corresponding subscriber's meter be shown scoring user gather R
i;
3) by described preliminary neighborhood N
ugather R with scoring user
ifront k the subscriber's meter occuring simultaneously is shown neighbor user set N
ui;
4) by described neighbor user set N
uiin each user to commodity I
igrade form be shown neighbor user scoring set F
ui;
In formula (1), Num
u(S
s) be described neighbor user scoring set F
uimiddle scoring S
sthe number of times occurring;
6) by described scoring S
swith the described scoring probability based on user
forming neighbor user predicts the outcome
In formula (2),
3. the mixing recommend method based on recommending probability fusion according to claim 1, is characterized in that: described project-based collaborative filtering method is to carry out as follows:
1) by all users to described commodity I
imark set and to the set of scoring of other commodity, utilize respectively constrained Pearson came relatedness metric method to obtain described commodity I
iwith the commodity similarity set of other commodity, other commodity are carried out to descending sort according to the similarity size in the set of described commodity similarity and obtain preliminary neighbours' commodity set N
i;
2) by user U
uin the set of marking
in corresponding commodity list be shown scoring commodity set R
u;
3) by described preliminary neighbours' commodity set N
iwith scoring commodity set R
ufront k the commodity list occuring simultaneously is shown neighbours' commodity set N
iu;
4) by user U
uto described neighbours' commodity set N
iuin the grade form of each commodity be shown neighbours' commodity scoring set F
iu;
5) utilize formula (3) to obtain project-based scoring probability
In formula (3), Num
i(S
s) be described neighbours' commodity scoring set F
iumiddle scoring S
sthe number of times occurring;
6) by described scoring S
swith described project-based scoring probability
formation project neighbours predict the outcome
In formula (4),
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310637512.4A CN103632290B (en) | 2013-12-02 | 2013-12-02 | A kind of based on the mixing recommendation method recommending probability fusion |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310637512.4A CN103632290B (en) | 2013-12-02 | 2013-12-02 | A kind of based on the mixing recommendation method recommending probability fusion |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103632290A true CN103632290A (en) | 2014-03-12 |
CN103632290B CN103632290B (en) | 2016-06-29 |
Family
ID=50213310
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201310637512.4A Active CN103632290B (en) | 2013-12-02 | 2013-12-02 | A kind of based on the mixing recommendation method recommending probability fusion |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103632290B (en) |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105761102A (en) * | 2016-02-04 | 2016-07-13 | 杭州朗和科技有限公司 | Method for predicting user commodity purchasing behavior and device thereof |
CN105761107A (en) * | 2016-02-15 | 2016-07-13 | 深圳市非零无限科技有限公司 | Method for acquiring target new users in internet products and device thereof |
CN105989510A (en) * | 2015-02-06 | 2016-10-05 | 展讯通信(上海)有限公司 | Online goods recommending system and device based on neural network, and mobile terminal |
CN106446195A (en) * | 2016-09-29 | 2017-02-22 | 北京百度网讯科技有限公司 | News recommending method and device based on artificial intelligence |
CN107133836A (en) * | 2017-03-22 | 2017-09-05 | 无锡中科富农物联科技有限公司 | A kind of adaptive weighting combined recommendation algorithm |
WO2017166990A1 (en) * | 2016-03-31 | 2017-10-05 | 深圳光启合众科技有限公司 | Artificial intelligence system having evaluation capability and evaluation method thereof |
CN107798045A (en) * | 2017-07-24 | 2018-03-13 | 中南大学 | User towards middle-size and small-size website, which accesses, is intended to acquisition methods and system |
CN108648072A (en) * | 2018-05-18 | 2018-10-12 | 深圳灰猫科技有限公司 | Internet finance lending risk evaluating system based on user credit dynamic grading |
CN109032591A (en) * | 2018-06-21 | 2018-12-18 | 北京航空航天大学 | A kind of crowdsourcing software developer recommended method neural network based |
CN110598092A (en) * | 2019-08-12 | 2019-12-20 | 深圳市天天学农网络科技有限公司 | Content recommendation method |
CN110689383A (en) * | 2019-10-12 | 2020-01-14 | 腾讯科技(深圳)有限公司 | Information pushing method and device, server and storage medium |
CN111291273A (en) * | 2020-02-20 | 2020-06-16 | 深圳前海微众银行股份有限公司 | Recommendation system optimization method, device, equipment and readable storage medium |
CN113221003A (en) * | 2021-05-20 | 2021-08-06 | 北京建筑大学 | Mixed filtering recommendation method and system based on dual theory |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102135999A (en) * | 2011-03-25 | 2011-07-27 | 南京财经大学 | User credibility and item nearest neighbor combination Internet recommendation method |
CN102982107A (en) * | 2012-11-08 | 2013-03-20 | 北京航空航天大学 | Recommendation system optimization method with information of user and item and context attribute integrated |
-
2013
- 2013-12-02 CN CN201310637512.4A patent/CN103632290B/en active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102135999A (en) * | 2011-03-25 | 2011-07-27 | 南京财经大学 | User credibility and item nearest neighbor combination Internet recommendation method |
CN102982107A (en) * | 2012-11-08 | 2013-03-20 | 北京航空航天大学 | Recommendation system optimization method with information of user and item and context attribute integrated |
Non-Patent Citations (2)
Title |
---|
YUANCHUN JIANG等: "Maximizing customer satisfaction through an online recommendation system:A novel associative classification model", 《DECISION SUPPORT SYSTEMS》 * |
谌彦妮: "基于用户—项目的混合协同过滤技术的应用研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105989510A (en) * | 2015-02-06 | 2016-10-05 | 展讯通信(上海)有限公司 | Online goods recommending system and device based on neural network, and mobile terminal |
CN105761102B (en) * | 2016-02-04 | 2021-05-11 | 杭州朗和科技有限公司 | Method and device for predicting commodity purchasing behavior of user |
CN105761102A (en) * | 2016-02-04 | 2016-07-13 | 杭州朗和科技有限公司 | Method for predicting user commodity purchasing behavior and device thereof |
CN105761107A (en) * | 2016-02-15 | 2016-07-13 | 深圳市非零无限科技有限公司 | Method for acquiring target new users in internet products and device thereof |
WO2017166990A1 (en) * | 2016-03-31 | 2017-10-05 | 深圳光启合众科技有限公司 | Artificial intelligence system having evaluation capability and evaluation method thereof |
CN106446195A (en) * | 2016-09-29 | 2017-02-22 | 北京百度网讯科技有限公司 | News recommending method and device based on artificial intelligence |
CN107133836A (en) * | 2017-03-22 | 2017-09-05 | 无锡中科富农物联科技有限公司 | A kind of adaptive weighting combined recommendation algorithm |
CN107798045A (en) * | 2017-07-24 | 2018-03-13 | 中南大学 | User towards middle-size and small-size website, which accesses, is intended to acquisition methods and system |
CN108648072A (en) * | 2018-05-18 | 2018-10-12 | 深圳灰猫科技有限公司 | Internet finance lending risk evaluating system based on user credit dynamic grading |
CN109032591A (en) * | 2018-06-21 | 2018-12-18 | 北京航空航天大学 | A kind of crowdsourcing software developer recommended method neural network based |
CN110598092A (en) * | 2019-08-12 | 2019-12-20 | 深圳市天天学农网络科技有限公司 | Content recommendation method |
CN110689383A (en) * | 2019-10-12 | 2020-01-14 | 腾讯科技(深圳)有限公司 | Information pushing method and device, server and storage medium |
CN110689383B (en) * | 2019-10-12 | 2023-08-22 | 腾讯科技(深圳)有限公司 | Information pushing method, device, server and storage medium |
CN111291273A (en) * | 2020-02-20 | 2020-06-16 | 深圳前海微众银行股份有限公司 | Recommendation system optimization method, device, equipment and readable storage medium |
CN113221003A (en) * | 2021-05-20 | 2021-08-06 | 北京建筑大学 | Mixed filtering recommendation method and system based on dual theory |
Also Published As
Publication number | Publication date |
---|---|
CN103632290B (en) | 2016-06-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103632290B (en) | A kind of based on the mixing recommendation method recommending probability fusion | |
CN101826114B (en) | Multi Markov chain-based content recommendation method | |
CN102982107B (en) | A kind of commending system optimization method merging user, project and context property information | |
CN103617289B (en) | Micro-blog recommendation method based on user characteristics and cyberrelationship | |
CN107833117A (en) | A kind of Bayes's personalized ordering for considering label information recommends method | |
CN104391849A (en) | Collaborative filtering recommendation method for integrating time contextual information | |
CN106327227A (en) | Information recommendation system and information recommendation method | |
CN107330727A (en) | A kind of personalized recommendation method based on hidden semantic model | |
CN106651519A (en) | Method and system for personalized recommendation based on label information | |
CN103995839A (en) | Commodity recommendation optimizing method and system based on collaborative filtering | |
CN103761237A (en) | Collaborative filtering recommending method based on characteristics and credibility of users | |
CN104239338A (en) | Information recommendation method and information recommendation device | |
CN104239399A (en) | Method for recommending potential friends in social network | |
CN103136683A (en) | Method and device for calculating product reference price and method and system for searching products | |
CN103824213A (en) | Individualized recommendation method based on user preferences and commodity properties | |
CN103593417A (en) | Collaborative filtering recommendation method based on association rule prediction | |
CN105138508A (en) | Preference diffusion based context recommendation system | |
CN103309967A (en) | Collaborative filtering method and system based on similarity propagation | |
CN104166732A (en) | Project collaboration filtering recommendation method based on global scoring information | |
CN105069072A (en) | Emotional analysis based mixed user scoring information recommendation method and apparatus | |
CN103678518A (en) | Method and device for adjusting recommendation lists | |
CN106610970A (en) | Collaborative filtering-based content recommendation system and method | |
CN107562947A (en) | A kind of Mobile Space-time perceives the lower dynamic method for establishing model of recommendation service immediately | |
CN106919699A (en) | A kind of recommendation method for personalized information towards large-scale consumer | |
Setiadi et al. | The impact of brand awareness and country of origin on purchase intention with mediation of self congruity on Chinese brand automotive products Wuling |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant |