CN102122291A - Blog friend recommendation method based on tree log pattern analysis - Google Patents
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Abstract
The invention discloses a blog friend recommendation method based on tree log pattern analysis. An offline digging method is adopted, a server log is analyzed to extract the access record of a visitor on a blog page; an access log tree which takes a blog to be recommended as a root is further constructed by the technologies of grouping, sorting, loop-back removal and the like; the constructed access log tree is frequently dug to find a frequent subtree conforming to a preset requirement; nodes in the frequent subtree serves as candidate blog friends; a recommendation degree is calculated according to the set formula; and friends with highest scores are recommended. An algorithm in the method is different from the traditional algorithm based on frequent item digging or frequent sequence digging; a frequent tree structure digging method is adopted for fully digging aiming at the specific parallel link relationship and indirect access characteristic of the blogosphere; and potential access contact among blogs is extracted and is recommended to an access user, thereby improving user experience. The blog friend recommendation method is an efficient and practical blog recommendation method.
Description
Technical field
The present invention relates to the data analysis technique of blog server daily record and the digging technology of frequent access pattern, particularly relate to a kind of blog intimate recommend method of analyzing based on tree-like logging mode.
Background technology
Along with the continuous development of Internet technology, blog has been not only the platform of a simple issue individual article, information, increased various types of as interaction functions such as message, concern, good friends after, can form a blog circle gradually between the user.Comprise good friend, potential good friend (not adding the blog of buddy list or good friend's good friend as yet) and like-minded other blogs or the like in the blog circle.In the so typical web2.0 of blog used, setting up like-minded user's social relationships was decision systems key of success, had therefore become the main functionality of blog system towards the friend recommendation of blog.The blog intimate exemplary application is by the visit behavior of user to blog, finds potential relevance between blog user, and try the suggestion blog according to relevance will with might its crowd be converted into the good friend and concern with common interest.
The blog circle is a kind of tree-like or graphic structure of complexity, has had some friend recommendation systems towards blog at present.They generally do recommendation based on the good friend's relation set up between blog and the visit capacity of server record, these recommend methods excavate based on frequent-item or frequent sequence, have the following disadvantages and shortcoming: 1) do not consider distinctive parallel linking relationship and dereference characteristic between blog; 2) do not consider logical relation between blog page that the sequencing of user to access pages hides; 3) do not take into full account the hierarchical relationship and the depth relationship of website organizational structure.
Summary of the invention
Abundant user behavior information and the page organizational information that daily record is implied at blog server, the object of the present invention is to provide a kind of blog intimate recommend method of analyzing based on tree-like logging mode, at the blog daily record, based on the blog recommend method of tree structure excavation.
The technical scheme that the present invention solves its technical matters employing is:
The step that this method adopts is as follows:
1) resolves original log, extract effective information, in database, create conversational list, be used for the access path of recording user;
2) at blog to be recommended, in database, find out the user of visit blog to be recommended excessively, the access log according to the user removes winding, and making up with blog to be recommended is the access log tree of root;
3) the access log tree that constructs is cooked frequent recurrence unordered tree and excavate, find out the frequent subtree that meets preset requirement;
4) the node in the frequent subtree as candidate's blog intimate, calculate by the formula degree of setting of recommendation, get the highest several of score value and recommend.
2, a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: resolve original log in the described step 1), extract effective information, be exactly with the daily record in the daily record resolver extraction server, obtain the Visitor Logs in the timeslice, remove the redundant information in user's request, change into visit tlv triple＜visitor, access time, accesses blog〉deposit in the conversational list, the selection of timeslice size is according to the performance of the computing machine of blog visit capacity and operation mining algorithm, the visitor is the registered user's, with user by name " visitor's " sign, the visitor is an anonymous, is the sign of " visitor " with User IP.
3, a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: described step 2) at blog to be recommended, in database, find out the user of visit blog to be recommended excessively, access log according to the user, remove winding, structure is the access log tree of root with blog to be recommended, be exactly institutional framework information according to the website, at blog to be recommended, in conversational list, find out the time that the user that visited this blog and user visit this blog for the first time, search the visitor who obtains at each, extract the record of searching other blog that the visitor that obtains visits after visit blog to be recommended; The tree structure maker is a unit structure access log tree with each visitor, the corresponding node of each blog of Accessor Access, each node comprises the visit triplet information, and the formation of father and son's node relationships is according to the temporal sequencing of connected reference request; For the winding that produces, delete limit at the latest on the access time, the access log tree of generation has three characteristics: the first, the access log tree has identical root node, is blog to be recommended; The second, there is not the identical brotgher of node of label in all access log trees; The 3rd, the access log tree is unordered, and promptly the child node of each node is unordered.
4, a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: in the described step 3) access log tree that constructs is cooked frequent recurrence unordered tree and excavate, find out the frequent subtree that meets preset requirement, exactly all access log trees are designated as t1 respectively, t2 ... tn selects suitable minimum support minsup (0,1), excavate with frequent subtree delver, concrete steps are as follows:
The first step, traversal t1, t2 ... tn is classified as same node point to " accesses blog " identical node in " visit tlv triple ", adds up the number of times fre1 that every kind of node occurs in access log tree, for fre1〉node of minsup*n, be designated as frequent subtree EQ1;
Second the step, EQ1 is done expansion, node among two EQ1 is done attended operation, constitute set membership, formation comprises the tree of 2 nodes, as candidate's subtree, count the occurrence number fre2 of candidate's subtree in all-access daily record tree, for fre2〉candidate's subtree of minsup*n, be designated as frequent subtree EQ2;
The 3rd the step, from EQ2, right wing footpath for every tree, do and enumerate expansion, node of each expansion is found out all possible candidate's subtree, counts occurrence number frei〉tree of minsup*n, be designated as new frequent subtree EQi, do similar recursive operation, constantly increase the interstitial content of the frequent subtree of excavating, till the candidate's subtree that does not meet.
5, a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: the node in the frequent subtree that in the described step 4) excavation is obtained is as candidate's blog intimate, calculate by the formula degree of setting of recommendation, getting the highest several of score value recommends, be exactly greater than 3 frequent subtree to the node number, fre sorts from big to small according to occurrence frequency, take out every frequent subtree successively, be done as follows: according to breadth first traversal, from the tree the 2nd layer, calculate the recommendation degree R of each node, formula is as follows:
Parameter declaration: fre is the frequency of frequent subtree; T represents whether there is direct page link, exists, and then T is 1, does not exist, and then T is 0; D is the degree of depth of this node, and the root node degree of depth is designated as 0; W
kBe every layer weight parameter, be defaulted as 1; B
kBe every layer number of branches, brotgher of node number under the promptly same father node; After calculating the recommendation degree of all both candidate nodes, as required, select the highest plurality of nodes of score value, get the blog of node correspondence and recommend as blog intimate.
The beneficial effect that the present invention has is:
To the visit behavior of blog and the design feature of blog website,,, excavate the frequent access pattern of tree structure according to the visitor at the access log of server in conjunction with existing data mining technology.The service provider of blog for the user recommends blog intimate, improves user experience according to the visit behavior of the frequent access model study analysis user of excavating; Simultaneously also can assist the web site architecture teacher to organize web site architecture better, improve the rate of people logging in of user blog.
Description of drawings
Fig. 1 is based on the overall construction drawing of the blog intimate recommend method of tree-like logging mode analysis.
Fig. 2 is access session and index thereof.
Fig. 3 is the access log tree that the session according to visitor1 among Fig. 2 constructs.
Fig. 4 is the synoptic diagram of frequent subtree method for digging.
Fig. 5 is the synoptic diagram of recommendation degree computing method.
Embodiment
The invention will be further described below in conjunction with instantiation and accompanying drawing.
By blog log analysis method provided by the present invention, can be quick, extract the frequent access pattern effectively, by intelligentized screening process potential blog intimate is recommended calling party, overall construction drawing as shown in Figure 1, concrete implementation step is as follows:
1) the daily record resolver among Fig. 1 is resolved the server log in the time period, the deletion redundant information, make up visit tlv triple＜visitor, access time, accesses blog〉(triple＜visitor, access_time, blog_url 〉), with the Apache Server daily record is example, and detailed process is as follows:
The daily record that is recorded in the Apache Server can be expressed as following form:
117.24.255.86?-?-?[01/Jul/2010:18:01:25?+0800]?"GET
http://B.blog.163.com?HTTP/1.0"?200?1231
"117.24.255.230.1277794615926482"?46807?"Mozilla/4.0?(compatible;?MSIE?6.0;?Windows?NT?5.1;?SV1;?.NET?CLR?2.0.50727;?.NET?CLR?3.0.04506.30)"
It is that the anonymous of 117.24.255.86 has been visited page http://A.blog.163.com in 01/Jul/2010:18:01:25+0800 time that this log record has provided IP.
So, can make up visit tlv triple＜117.24.255.86 successively, 2010-7-1 18:01:25, blogA 〉, be the registered user for the visitor, with the sign of registration ID as visitor; For anonymous, as differentiation, create an interim ID with IP; For the page of visit, for the ease of next step processing, can the url address of the page be simplified in conjunction with the institutional framework information of website, as http://A.blog.163.com being reduced to blogA here, both must be corresponding one by one.
2) at blog to be recommended, the tree structure maker among Fig. 1 is found out the user of visit blog to be recommended excessively in database, and the access log according to the user removes winding, and making up with blog to be recommended is the access log tree of root, and concrete steps are as follows:
The first step, institutional framework information according to the website, at certain blog blogA to be recommended, the packet sequencing module of tree structure maker finds out the all-access (SQL query: select visitor, distinct access_time from triple where access_url=blogA) of spending the time that the user of blogA and this user visit blogA for the first time in conversational list.Suppose that the time that user visitor0 visits blogA for the first time is access_time0, search the page of all access times after access_time0 of user visitor0, the user that each inquiry is obtained does same operation (SQL query: select visitor, access_time, access_url from triple where visitor=visitor0 and access_time〉access_time0, through above operation, can obtain user session information as shown in Figure 2
Second step to the record that inquiry obtains, was a unit structure access log tree with visitor, and the formation of father and son's node relationships is according to the temporal sequencing of connected reference request; For the winding that produces, the winding module of going in the tree structure maker is eliminated winding by deletion time that limit at the latest of swinging fore-upward, and the access log tree that produces according to the session of visitor1 among Fig. 2 as shown in Figure 3.The access log tree that produces has three characteristics: the first, and all access log trees have identical root node, are blog to be recommended; The second, there is not the identical node of label in all access log trees; The 3rd, the access log tree is unordered, does not promptly consider the sequencing between the brotgher of node.
3) frequent subtree delver shown in Figure 1 access log tree that previous step is constructed is cooked frequent recurrence unordered tree and excavates, find out the frequent subtree that meets preset requirement, concrete steps are as follows: the tree structure maker is respectively t1 to the access log tree numbering that previous step obtains, t2 ... tn.
The first step: the candidate's subtree generation module in the tree structure maker travels through all access log trees, " accesses blog " identical node in " visit tlv triple " is classified as same node point, position that every kind of node of subtree frequency statistics module statistics occurs in the access log tree and the total fre1(frequency that contains the tree of this kind node), for fre1〉node of minsup*n, be designated as frequent subtree EQ1;
Second step: the node among the EQ1 is done attended operation in twos, constitute set membership,, count the number of times fre2 that candidate's frequent subtree occurs in all daily records as candidate's frequent subtree, concrete steps as shown in Figure 4, node A and Node B all belong to EQ1, and to A, B does attended operation, A is the father node of B, the last new position (in Fig. 4 be B node) of node in elite tree of adding of record simultaneously is for fre2〉candidate's subtree of minsup*n, be designated as frequent subtree EQ2.
The 3rd step: from EQ2,, do and enumerate expansion, expand a node at every turn, find out all possible candidate's subtree, count occurrence number frei for the right wing footpath of every tree〉tree of minsup*n, be designated as new frequent subtree EQi.As shown in Figure 4, at first done the expansion in right wing footpath, expanded a new Node B, also can do expansion, but once can only expand a node original B node for node A.So do similar recursive operation, constantly increase the interstitial content of the frequent subtree of excavating, till the frequent subtree of the candidate who does not meet.Excavate in the process of tree, record for the ease of tree, adopted string encoding to tree, for example tree t1 is encoded to ABC-1BD-1E-1-1B among Fig. 4, the character code of t2 is ABC-1DE-1-1-1B, and coding inserts one-1 according to the depth-first traversal order when going back at every turn, according to this method, tree and string encoding are one to one.
4) excavate all frequent subtrees after, both candidate nodes recommended device shown in Figure 1 sorts from big to small according to the occurrence frequency fre of frequent subtree, taking out every frequent subtree successively is done as follows: according to the breadth first traversal order, from the tree the 2nd layer, node recommendation degree computing module calculates the recommendation degree R of each node, and formula is as follows:
Parameter declaration: fre is the frequency of frequent subtree; T represents whether there is direct page link, exists, and then T is 1, does not exist, and then T is 0; D is the degree of depth of this node, and the root node degree of depth is designated as 0;
Be every layer weight parameter, be defaulted as 1;
Be every layer number of branches, brotgher of node number under the promptly same father node.
As shown in Figure 5, excavated frequent subtree ABC-1D-1-1B(string encoding), this tree is at t1, all occur among the t2, so frequency fre is 100%, calculate the recommendation degree R of this candidate's subtree, step is as follows: for an A, at the 1st layer that sets, so skip over, for the Node B of the second layer, if do not have the direct link of A in the website structure to B, then T is 0, so R
B=0; If have the direct link of A, T=1, then R in the website structure to B
B=1*1*1/2=0.5.For node C, if the direct link that do not exist Node B to arrive node C, T=0 then, thereby R
C=0; If exist, then T=1, then R
C=1*1*(1/2) (1/3)=0.167.The situation of node D is identical with C.
After calculating the recommendation degree of all both candidate nodes, as required, select the blog of the highest plurality of nodes correspondence of score value and recommend as blog intimate.Press the node that Fig. 5 calculates, if all there is directly link, according to calculating, the B node, the recommendation degree of E node all is 0.5, so the blog of these two node correspondences is at first recommended as blog intimate, the recommendation degree of node C and node D is 0.167, if desired, the blog of their correspondences is further recommended as the blog intimate quilt.
Claims (5)
1. blog intimate recommend method of analyzing based on tree-like logging mode is characterized in that the step that this method adopts is as follows:
1) resolves original log, extract effective information, in database, create conversational list, be used for the access path of recording user;
2) at blog to be recommended, in database, find out the user of visit blog to be recommended excessively, the access log according to the user removes winding, and making up with blog to be recommended is the access log tree of root;
3) the access log tree that constructs is cooked frequent recurrence unordered tree and excavate, find out the frequent subtree that meets preset requirement;
4) the node in the frequent subtree as candidate's blog intimate, calculate by the formula degree of setting of recommendation, get the highest several of score value and recommend.
2. a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: resolve original log in the described step 1), extract effective information, be exactly with the daily record in the daily record resolver extraction server, obtain the Visitor Logs in the timeslice, remove the redundant information in user's request, change into visit tlv triple＜visitor, access time, accesses blog〉deposit in the conversational list, the selection of timeslice size is according to the performance of the computing machine of blog visit capacity and operation mining algorithm, the visitor is the registered user's, with user by name " visitor's " sign, the visitor is an anonymous, is the sign of " visitor " with User IP.
3. a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: described step 2) at blog to be recommended, in database, find out the user of visit blog to be recommended excessively, access log according to the user, remove winding, structure is the access log tree of root with blog to be recommended, be exactly institutional framework information according to the website, at blog to be recommended, in conversational list, find out the time that the user that visited this blog and user visit this blog for the first time, search the visitor who obtains at each, extract the record of searching other blog that the visitor that obtains visits after visit blog to be recommended; The tree structure maker is a unit structure access log tree with each visitor, the corresponding node of each blog of Accessor Access, each node comprises the visit triplet information, and the formation of father and son's node relationships is according to the temporal sequencing of connected reference request; For the winding that produces, delete limit at the latest on the access time, the access log tree of generation has three characteristics: the first, the access log tree has identical root node, is blog to be recommended; The second, there is not the identical brotgher of node of label in all access log trees; The 3rd, the access log tree is unordered, and promptly the child node of each node is unordered.
4. a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: in the described step 3) access log tree that constructs is cooked frequent recurrence unordered tree and excavate, find out the frequent subtree that meets preset requirement, exactly all access log trees are designated as t1 respectively, t2 ... tn selects suitable minimum support minsup (0,1), excavate with frequent subtree delver, concrete steps are as follows:
The first step, traversal t1, t2 ... tn is classified as same node point to " accesses blog " identical node in " visit tlv triple ", adds up the number of times fre1 that every kind of node occurs in access log tree, for fre1〉node of minsup*n, be designated as frequent subtree EQ1;
Second the step, EQ1 is done expansion, node among two EQ1 is done attended operation, constitute set membership, formation comprises the tree of 2 nodes, as candidate's subtree, count the occurrence number fre2 of candidate's subtree in all-access daily record tree, for fre2〉candidate's subtree of minsup*n, be designated as frequent subtree EQ2;
The 3rd the step, from EQ2, right wing footpath for every tree, do and enumerate expansion, node of each expansion is found out all possible candidate's subtree, counts occurrence number frei〉tree of minsup*n, be designated as new frequent subtree EQi, do similar recursive operation, constantly increase the interstitial content of the frequent subtree of excavating, till the candidate's subtree that does not meet.
5. a kind of blog intimate recommend method of analyzing based on tree-like logging mode according to claim 1, it is characterized in that: the node in the frequent subtree that in the described step 4) excavation is obtained is as candidate's blog intimate, calculate by the formula degree of setting of recommendation, getting the highest several of score value recommends, be exactly greater than 3 frequent subtree to the node number, fre sorts from big to small according to occurrence frequency, take out every frequent subtree successively, be done as follows: according to breadth first traversal, from the tree the 2nd layer, calculate the recommendation degree R of each node, formula is as follows:
Parameter declaration: fre is the frequency of frequent subtree; T represents whether there is direct page link, exists, and then T is 1, does not exist, and then T is 0; D is the degree of depth of this node, and the root node degree of depth is designated as 0; W
kBe every layer weight parameter, be defaulted as 1; B
kBe every layer number of branches, brotgher of node number under the promptly same father node; After calculating the recommendation degree of all both candidate nodes, as required, select the highest plurality of nodes of score value, get the blog of node correspondence and recommend as blog intimate.
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