CN103514239A - Recommendation method and system integrating user behaviors and object content - Google Patents
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Abstract
The invention discloses a recommendation method and system integrating user behaviors and object content. The recommendation method integrating the user behaviors and the object content comprises the steps that through correlation between behavior data of each user and content data of all the objects, a list of user interest of each user to all the objects is obtained; the similarity of the list of user interest and the content data of all the objects is calculated, the recommendation object weight of each user to the content data of each object is obtained, and the objects recommended to each user are obtained through ranking of the recommendation object weights. According to the recommendation method and system integrating the user behaviors and the object content, the main problems that how an existing recommendation conducts cold start and how the existing recommendation smoothly transmits to the normal operation state from cold start are solved, the accuracy, the coverage rate, the novelty and the like of the system are greatly improved, the accuracy and the personalization of the recommendation system can be well improved, and more users are attracted and use the recommendation system.
Description
Technical field
The present invention relates to intelligent recommendation system, in particular recommend method and the system of a kind of integrated user behavior and article content.
Background technology
Along with the development of infotech and internet, how from a large amount of information, to find the interested information of user, how to allow oneself information be subject to users' welcome, for user and provider, be all a very difficult thing.The task of commending system is exactly contact user and information, helps on the one hand user to find own valuable information, and allows on the other hand information can be presented in face of its interesting user, thereby realize information consumer and informant's doulbe-sides' victory.Commending system is mainly the behavior by analysis user, to its modeling, thus the interest of predictive user recommending to user.
According to the different modes of deal with data, the method that commending system is used mainly can be divided into collaborative filtering and information filtering, if commending system only utilizes user's behavioral data, according to user's historical interest, to user, recommend, so this method is called as collaborative filtering (collaborative filtering).If commending system has utilized the content-data (content filtering) of article, calculate user's interest and the similarity between article description, to user, recommend, be called information filtering.Collaborative filtering is the foremost algorithm in commending system field, this algorithm mainly the historical behavior by research user to user's interest modeling and make recommendation to user.It mainly comprises the collaborative filtering based on user, the collaborative filtering based on article, and the model based on theme.Information filtering is to user, to recommend and the article of liking before them similar other article in terms of content on the basis based on article content.Content mainly comprises some attributes of article.This method can be well understood to user's point of interest more, thereby better explains the reason of recommending.
Collaborative filtering mainly depends on historical behavior data, and the therefore good cold start-up problem of resolution system that is to say that these algorithms cannot recommend to new user, because there is no relevant behavioral data.Also new article cannot be recommended may be to its interested user simultaneously.Information filtering algorithm is mainly to utilize the similarity of content between article to recommend to user, and it only depends on the data of article content itself, so can solve cold start-up problem.But because information filtering algorithm has been ignored user behavior, thereby also ignored the rule comprising in the popularity of article and user behavior, so its ratio of precision collaborative filtering is low.The advantage that how to merge these two kinds of algorithms designs a kind of new proposed algorithm cold start-up facing for commending system and the precision that improves commending system and is of great significance.
Therefore, prior art has yet to be improved and developed.
Summary of the invention
The technical problem to be solved in the present invention is, above-mentioned defect for prior art, recommend method and the system of a kind of integrated user behavior and article content are provided, solve the cold start-up of existing commending system and how from cold start-up smoothly excessively to the problem of normal operating condition.
The technical scheme that technical solution problem of the present invention adopts is as follows:
A recommend method for integrated user behavior and article content, wherein, comprises the following steps:
A, by associated each user's behavioral data and the content-data of all article, obtain the user interest list of each user to all article contents;
B, calculate the similarity of the content-data of described user interest list and described all article, draw the recommendation article weight of each user to the content-data of article, and by drawing each user's recommendation article to recommending article weight to sort.
Described integrated user behavior and the recommend method of article content, wherein, described steps A specifically comprises:
A1, respectively the content-data of user's behavioral data and article is adopted to user's behavior vector and the content vector representation of article, and the content vector of the behavior vector sum article of the method associated user by matrix decomposition, obtain user's interest vector.
Described integrated user behavior and the recommend method of article content, wherein, described step B specifically also comprises:
By the content-data of all article, upgrade the content-data of newly-increased article, and by calculating the similarity of the content-data of described newly-increased article and all users' behavioral data, draw the recommendation user weight of newly-increased article to user's behavioral data, and by draw the recommendation user of newly-increased article to recommending article weight to sort.
Described integrated user behavior and the recommend method of article content, wherein, calculate user's interest vector by following formula:
Wherein,
the behavior vector u that represents unique user,
the content vector c that represents Individual Items,
the content matrix C that represents all article,
the interest vector f that represents unique user, n and m are natural number,
;
When the content matrix of all article is fixedly time, by above-mentioned formula (1), derive the interest vector f of unique user.
Described integrated user behavior and the recommend method of article content, wherein, when Add User and described in Add User while producing behavioral data, the behavior vector Adding User by the behavioral data Adding User, and draw by above-mentioned formula (1) interest vector Adding User, and derive by following formula (2) the recommendation article weight vectors Adding User:
Wherein,
represent to recommend article weight vectors, by above-mentioned recommendation article weight vectors, obtain and recommend article to recommend user.
Described integrated user behavior and the recommend method of article content, wherein, when having newly-increased article, upgrade the content vector of newly-increased article, and by formula (3), derive the recommendation user weight vectors of newly-increased article according to the content matrix C of described all article:
formula (3)
Wherein,
represent described user behavior matrix U,
represent to recommend user's weight vectors, by above-mentioned recommendation user weight vectors, obtain the recommendation user of newly-increased article.
Described integrated user behavior and the recommend method of article content, wherein, described steps A also comprises: the interest vector that adopts the data structure calculating unique user of sparse storage.
A commending system for integrated user behavior and article content, wherein, described system comprises:
Relating module, for by associated each user's behavioral data and the content-data of all article, obtains the user interest list of each user to all article contents;
Recommend article module, for calculating the similarity of the content-data of described user interest list and described all article, draw the recommendation article weight of each user to the content-data of article, and by drawing each user's recommendation article to recommending article weight to sort.
Described integrated user behavior and the commending system of article content, wherein, described system comprises:
Recommend line module, for the content-data by all article, upgrade the content-data of newly-increased article, and by calculating the similarity of the content-data of described newly-increased article and all users' behavioral data, draw the recommendation user weight of newly-increased article to user's behavioral data, and by draw the recommendation user of newly-increased article to recommending article weight to sort.
Recommend method and the system of integrated user behavior provided by the present invention and article content, effectively solved the subject matter that current commending system faces, cold start-up such as commending system, and how from cold start-up smoothly excessively to normal operating condition etc., rate of accurateness, coverage rate, novel degree etc. have greatly been improved, can better improve precision and the personalization of commending system, attract more user to use.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the recommend method of integrated user behavior provided by the invention and article content.
Fig. 2 is the structural representation of the commending system of integrated user behavior provided by the invention and article content.
Embodiment
For making object of the present invention, technical scheme and advantage clearer, clear and definite, referring to accompanying drawing, developing simultaneously, the present invention is described in more detail for embodiment.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
Referring to Fig. 1, Fig. 1 is the process flow diagram of the recommend method of integrated user behavior provided by the invention and article content, comprises the following steps:
Step S100, by associated each user's behavioral data and the content-data of all article, obtain the user interest list of each user to all article contents;
Step S200, calculate the similarity of the content-data of described user interest list and described all article, draw the recommendation article weight of each user to the content-data of article, and by drawing each user's recommendation article to recommending article weight to sort.
Below in conjunction with specific embodiment, above-mentioned steps is described in detail.
The present invention carrys out the interest modeling to user by the content-data of integrated article and user's behavioral data, thereby effectively in conjunction with the advantage of information filtering and collaborative filtering, user is recommended.Particularly, the present invention is mainly divided into off-line and online two parts, off-line part is mainly in order to obtain user's interest list, online part is the user interest list by partly obtaining at off-line, provides its corresponding recommendation article and when newly-increased article, provide its corresponding user of recommendation when Adding User.
In off-line part, in order to obtain user's interest list, first user's interest is carried out to modeling, define a topic model, in order to obtain user's interest list.By the content-data of associated article and user's behavioral data, topic model utilizes the data mining algorithm of matrix decomposition to obtain the interest list of user to article content.At off-line, partly obtain user as follows to the main flow process of the interest list of article content: first respectively the content-data of user's behavioral data and article is adopted to user's behavior vector and the content vector representation of article, the behavior vector u of unique user is expressed as
,
All users' behavioural matrix U=
, the content vector c of Individual Items is expressed as
, the content matrix of all article
, the interest vector f of unique user uses
represent, topic model is like this:
The content-data of existing subscriber's behavioral data and article is obtained to user's interest list by topic model; And if have new user behavior or an input of new article content, upgrade the vector space model of all user behavior datas or all article content-data, to obtain more accurately the weight of its feature in overall vector space model, then by topic model, go to obtain the user interest list after renewal like this.
In online part, set up the recommended models of commending system, recommended models is mainly weighed the fancy grade of user to each article by user's interest list, and provides online recommendation results.Here, recommended models is divided into two types, and one is the recommendation article model for user, and one is the recommendation user model for article.When recommended models is defined, according to the difference of recommended models type, carry out different processing.When user is recommended to article, if existing user, can directly inquire this user's interest list, then by recommending article model to recommend online article; If Add User and produced corresponding behavioral data, in order to obtain exactly the weight of this behavioral data Adding User in the overall situation, first the behavioral data by all users removes to upgrade the behavioral data that this Adds User, and then by topic model, is gone to obtain its user interest list and is provided recommendation article.When newly-increased article are recommended user, if there is the content-data of newly-increased article, first by all the elements data, remove to upgrade the content-data of these newly-increased article, then by all users' interest list and recommendation user model, go to obtain recommendation user.
Particularly, recommend article model as follows:
Recommendation user model is as follows:
Concrete recommendation process is: when Add User and described in Add User while producing behavioral data, the behavior vector Adding User by the behavioral data Adding User, and by drawing the interest vector Adding User, and the recommendation article weight vectors that goes out to Add User by following recommendation article model inference, is obtained and is recommended article to recommend user by above-mentioned recommendation article weight vectors.When having newly-increased article, according to the content matrix C of described all article, upgrade the content vector of newly-increased article, and by recommending user model to derive the recommendation user weight vectors of newly-increased article, then by above-mentioned recommendation user weight vectors, obtain the recommendation user of newly-increased article.
By above-mentioned two recommended models, can draw respectively the weight vectors of recommending article and the weight vectors of recommending user, according to recommending the weight vectors of article and recommending user's weight vectors can provide very accurately recommendation article and recommend user, the recommendation article here and recommendation user are not limited to recommend one, can recommend a plurality of.By the hobby of interest list analysis user, draw good rationale for the recommendation.
The present invention adopts matrix decomposition method to obtain user's interest list, and concrete computation process is as follows: the content-data of article and user's behavioral data represent with vector space model.The content weight vectors of each article can be expressed as
, each user's behavior vector can be expressed as
, can be by TF-IDF(Term Frequency – Inverse Document Frequency, the anti-document frequency of Ci Pin –) and method calculates, the error to avoid calculating by these vectorial regularizations simultaneously;
TF(word frequency) number of times that calculated characteristics occurs in the vector of current object, DF(document frequency) calculating feature in whole vector space appears in how many different objects.When having new user behavior data or new article content-data to add, will upgrade according to this computation model.
Definition user's interests matrix is F, in order to obtain F, and objective definition function J:
Wherein,
it is Frobenius norm.For asking
,
can be rewritten as:
By using Lagrangian function to solve this condition objective function J.Because
matrix is fixing, therefore a demand solution
.Suppose that its corresponding Lagrange multiplier is
, LagrangianL is defined as:
Right
ask local derviation to obtain
Therefore,
From above-mentioned formula, can find out, objective function is monotone decreasing convergence.When initialization,
can be some random numbers, but in order to obtain unique user interest list, our initialization
for being 1 matrix entirely, that is to say that we think that user is consistent to all interest before obtaining real user interest vector.Along with the constantly iteration of algorithm,
little by little convergence.The end condition of algorithm can arrange certain iterations, or the error precision of working as objective function is within certain scope.
The number of simultaneously considering user and article is very huge, and user behavior data and article content-data matrix are very sparse, in order to improve the speed of calculating, reduce the consumption of internal memory simultaneously, adopt sparse storage mode, data structure by sparse storage is calculated the interest vector of unique user, is about to the value of non-zero and its and quotes two arrays and store, and can effectively carry out the computing of sparse matrix like this, solve large matrix computing, obtain rapidly recommendation list.
At the above-mentioned unique user of giving
while recommending article, need to calculate user's interest vector
content matrix with all article
between recommendation article weight vectors
, due to
with
all immobilizations, so their product cosine similarity namely.By recommendation article weight vectors is sorted to obtain maximally related article.In like manner, in the time increasing article newly and recommend user, calculate the content vector of newly-increased article
interests matrix with all users
between recommendation user weight vectors
.Then it being sorted to obtain the user of high several weights recommends.
Further, the behavioral data of the user in the present invention can have multiple expression-form, such as dominant feedback or stealthy feedback, shows that feedback can be that user is to the scoring of article and stealthy feedback can browsing record and represent from user.And the content-data of article can be used the label of article, textual description of article etc.Which kind of representation no matter, the model that the present invention proposes takes full advantage of various data representation form and recommends, and the whole process of disposal system from cold start-up to normal operation effectively, thereby has practicality widely.
Based on above-mentioned recommend method, the present invention also provides the commending system of a kind of integrated user behavior and article content, and as shown in Figure 2, described system comprises:
Relating module 10, for by associated each user's behavioral data and the content-data of all article, obtains the user interest list of each user to all article contents;
Recommend article module 20, for calculating the similarity of the content-data of described user interest list and described all article, draw the recommendation article weight of each user to the content-data of article, and by drawing each user's recommendation article to recommending article weight to sort.
Recommend line module 30, for the content-data by all article, upgrade the content-data of newly-increased article, and by calculating the similarity of the content-data of described newly-increased article and all users' behavioral data, draw the recommendation user weight of newly-increased article to user's behavioral data, and by draw the recommendation user of newly-increased article to recommending article weight to sort.
Preferably, commending system provided by the invention can be widely used in various intelligent recommendation systems, such as film commending system, music recommend system, reading commending system etc.
In sum, recommend method and the system of integrated user behavior provided by the invention and article content, by data mining model effectively in conjunction with the advantage of this behavior recommendation and commending contents, effectively solve the subject matter that current commending system faces, such as the cold start-up of commending system, and how from cold start-up smoothly excessively to normal operating condition etc.The recommend method of integrated user behavior of the present invention and article content and system can overcome not to be had content or there is no these two kinds of special circumstances of behavior, and rate of accurateness, coverage rate and novel degree etc. have greatly been improved, can better improve precision and the personalization of commending system, attract more user to use.
Should be understood that, application of the present invention is not limited to above-mentioned giving an example, and for those of ordinary skills, can be improved according to the above description or convert, and all these improvement and conversion all should belong to the protection domain of claims of the present invention.
Claims (9)
1. a recommend method for integrated user behavior and article content, is characterized in that, comprises the following steps:
A, by associated each user's behavioral data and the content-data of all article, obtain the user interest list of each user to all article contents;
B, calculate the similarity of the content-data of described user interest list and described all article, draw the recommendation article weight of each user to the content-data of article, and by drawing each user's recommendation article to recommending article weight to sort.
2. the recommend method of integrated user behavior according to claim 1 and article content, is characterized in that, described steps A specifically comprises:
A1, respectively the content-data of user's behavioral data and article is adopted to user's behavior vector and the content vector representation of article, and the content vector of the behavior vector sum article of the method associated user by matrix decomposition, obtain user's interest vector.
3. the recommend method of integrated user behavior according to claim 1 and article content, is characterized in that, described step B specifically also comprises:
By the content-data of all article, upgrade the content-data of newly-increased article, and by calculating the similarity of the content-data of described newly-increased article and all users' behavioral data, draw the recommendation user weight of newly-increased article to user's behavioral data, and by draw the recommendation user of newly-increased article to recommending article weight to sort.
4. the recommend method of integrated user behavior according to claim 2 and article content, is characterized in that, calculates user's interest vector by following formula:
Wherein,
the behavior vector u that represents unique user,
the content vector c that represents Individual Items,
the content matrix C that represents all article,
the interest vector f that represents unique user, n and m are natural number,
;
When the content matrix of all article is fixedly time, by above-mentioned formula (1), derive the interest vector f of unique user.
5. the recommend method of integrated user behavior according to claim 4 and article content, it is characterized in that, when Add User and described in Add User while producing behavioral data, the behavior vector Adding User by the behavioral data Adding User, and draw by above-mentioned formula (1) interest vector Adding User, and derive by following formula (2) the recommendation article weight vectors Adding User:
Wherein,
represent to recommend article weight vectors, by above-mentioned recommendation article weight vectors, obtain and recommend article to recommend user.
6. the recommend method of integrated user behavior according to claim 4 and article content, it is characterized in that, when having newly-increased article, according to the content matrix C of described all article, upgrade the content vector of newly-increased article, and by formula (3), derive the recommendation user weight vectors of newly-increased article:
formula (3)
Wherein,
represent described user behavior matrix U,
represent to recommend user's weight vectors, by above-mentioned recommendation user weight vectors, obtain the recommendation user of newly-increased article.
7. the recommend method of integrated user behavior according to claim 2 and article content, is characterized in that, described steps A also comprises: the interest vector that adopts the data structure calculating unique user of sparse storage.
8. a commending system for integrated user behavior and article content, is characterized in that, described system comprises:
Relating module, for by associated each user's behavioral data and the content-data of all article, obtains the user interest list of each user to all article contents;
Recommend article module, for calculating the similarity of the content-data of described user interest list and described all article, draw the recommendation article weight of each user to the content-data of article, and by drawing each user's recommendation article to recommending article weight to sort.
9. the commending system of integrated user behavior according to claim 8 and article content, is characterized in that, described system comprises:
Recommend line module, for the content-data by all article, upgrade the content-data of newly-increased article, and by calculating the similarity of the content-data of described newly-increased article and all users' behavioral data, draw the recommendation user weight of newly-increased article to user's behavioral data, and by draw the recommendation user of newly-increased article to recommending article weight to sort.
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