CN103106259B - A kind of mobile webpage content recommendation method based on situation - Google Patents
A kind of mobile webpage content recommendation method based on situation Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 38
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- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 238000011524 similarity measure Methods 0.000 description 2
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Abstract
The present invention relates to a kind of on the mobile terminal device according to the content recommendation method that current context provides individualized webpage to browse for user: contextual information when recording user browses mobile webpage at every turn and the type of institute's browsing content, same web page browsing history in user's browsing history is counted, calculate the similarity of situation in current context and user preference table, result of calculation according to context aware degree reorders to user preference table from high to low, then can be user recommend related web page contents according to ranking results.Context aware technology is dissolved in the personalized browse application of mobile webpage by the present invention, can provide customized information more accurately, improve Consumer's Experience further for user.
Description
Technical field
The present invention relates to a kind of on the mobile terminal device according to current context for user provides the concrete grammar of commending contents in individualized webpage browsing service process.
Background technology
Along with the development of society and the progress of science and technology, Internet technology is just flourish with unpredictable speed, and steps into huge numbers of families gradually, is deep in the middle of daily life, study and work.Internet is a large amount of colourful information for people provide, and relates to various field and comprise news, scientific research, education, amusement etc., and these Internet resources quantity also present the trend of exponential increase simultaneously.Due to the quick growth of internet size and scale, its content and type also become and become increasingly complex and variation.When user browses webpage, usually can flood by the network information of magnanimity, in the process finding oneself interested content, usually to spend more energy and time.Personalized service is a kind of pointed method of service, customer-centric, by analyzing the point of interest of user or preference, provides and recommend the information of being correlated with for user.Personalized network browsing can be only user for individual consumer and push its interested content, ignores other information, and then alleviates the situation of information overflow when user browses webpage, improves Consumer's Experience.
In recent years, universal along with mobile intelligent terminal equipment, by mobile terminal device particularly smart mobile phone accessing Internet obtaining information become more and more general.Because mobile intelligent terminal equipment has, screen is little, network connects the features such as Bandwidth-Constrained, and the focus more becoming research is browsed in the personalization of therefore moving webpage, on the one hand, facilitates user to read its interested content, reduces user and diverts one's attention; On the other hand, only load the interested content of user by server, the display of web page contents can be accelerated, also can save flow, save expenses of surfing Internet.
Context aware technology can provide technical support for the web browsing experience of personalization.Situation is used to describe the information of substance feature, and entity comprises people, thing and various with user or apply mutual object.Context aware is exactly perception entity or user identity, Location, and what current time, does, the process how to do.And these information, usually need to be obtained by multiple means, mobile intelligent terminal equipment is that context aware provides hardware supported.Mobile intelligent terminal now mostly embedded in multiple sensors and comprises acceleration transducer, GPS etc.Intelligent terminal can perception user context, and then analyzes active user and individualized feature thereof, comprises customer location, current time, behavior act etc.By analyzing user and situation thereof, user personalized information can be obtained, finally for user provides personalized service.During personalization context aware technology being dissolved into mobile webpage is browsed, the movability that can make full use of mobile terminal on the one hand obtains multiple user context, can provide customized information more accurately on the other hand, improve Consumer's Experience further for user.
Webpage personalization is browsed and how to be realized by recommended technology, traditional recommended technology comprises collaborative filtering recommending, content-based recommendation and knowledge type recommendation etc., and the recommended technology in modern times then includes based on the recommendation of context aware, semantic recommendation, cross-domain recommendation etc.Occur that some move the personalized method browsed of webpage at present, mostly be by acquisition of information user preferences such as user's registration information, web page browsing record and comments, multiple user is analyzed, the method of collaborative filtering is adopted to recommend the interested content of user, as patent 200910089587.7 for user.Patent 201110023436.9 on this basis, with the addition of position module, for user recommends the related news of current location, permanent residence or potential destination, and does not consider the effect of user's historical position information to user preference.In the above patent, the first, user must register, and by analyzing multiple registered user, adopts the method for collaborative filtering to carry out commending contents for individual consumer; The second, when obtaining user preference, not considering the contextual information of user, comprising the behavior etc. of time, place and user.Therefore, if webpage does not provide user to register or the function such as identification, be that individual consumer pushes customized information more accurately, improve Consumer's Experience further, then need fully to use user context information analysis user preference, realize content-based recommendation.
Summary of the invention
The technical matters solved
In order to avoid the deficiencies in the prior art part, the present invention proposes a kind of content recommendation method browsed based on the mobile webpage personalization of situation.The method can make full use of mobile device and obtain user context, by scenario analysis user preference and behavior, for user pushes the interested content of different users under different situation, improves the Consumer's Experience that user reads mobile webpage.
Technical scheme
Based on a mobile webpage content recommendation method for situation, it is characterized in that step is as follows:
Step 1: contextual information when recording user browses mobile webpage at every turn and the type of institute's browsing content, when user often clicks a web page contents, the contextual information of current web page and the type of click on content to be sent in server database with user network page browsing historical record and to preserve; Described contextual information is date, when and where;
Described user network page browsing historical record represents:
R_history＝(C
1,C
2,…,C
n,Conent)
Wherein, C
krepresent the different contextual information of user, k represents the number of situation, and Content is the type of the web page contents that user clicks;
Step 2: the same web page browsing history in user's browsing history is counted, represent that user clicks the number of times of dissimilar content respectively under different situation with Clicktimes, the user preference table of generation is:
R_preference＝(C
1,C
2,…,C
n,Conent，Clicktimes)；
Step 3: open in the process of webpage by mobile terminal device user, obtains the current context CTX of user by this equipment
cur=(C
1, C
2..., C
n), adopt following context aware degree computing formula to calculate the similarity of situation in current context and user preference table,
Wherein: s
k(x, y) is point situation C of situation x and y
ksimilarity s
k(x, y); δ
krepresent corresponding situation C
kthe validity of data, effectively then value is 1, invalid, and value is 0; Divide situation C
kon the weight w of user preference impact
krepresent;
Step 4: the result of calculation according to context aware degree reorders to user preference table from high to low, again according to click volume to content orderings different under same situation, in user preference table, the web page contents type of Section 1 is then the content most interested under current context of user; The data repeated in content type in filter table, obtain the sequence of user preference under current context.Then can be user according to ranking results and recommend related web page contents.
Described contextual information is obtained by terminal device embedded sensors or clock.
The webpage that described user browses needs to carry out predefine, uses <div></divGreatT .GreaT.GT that web page contents is divided into different semantic chunk, and marks with label.User often clicks a web page contents, can obtain Current Content generic, i.e. Content.
Weight w in described step 3
kcalculation procedure as follows:
Step (1): calculate interest ratio
with the click volume Clicktime (Content of different content
i) ratio that accounts for total click volume Totalclicktime is interest ratio
wherein Content
irepresent different web page contents types;
Step (2): calculate situation C
kcorresponding Content
ithe variance of click volume
Step (3): by different content at situation C
kunder interest ratio be multiplied by corresponding variance, then can try to achieve situation C
kinterest variance
Step (4): different situation C
kshared weight is
Beneficial effect
The invention has the beneficial effects as follows: the present invention proposes a kind of on the mobile terminal device according to the content recommendation method that current context provides individualized webpage to browse for user.Context aware technology is dissolved in the personalized browse application of mobile webpage, customized information more accurately can be provided for user, improve Consumer's Experience further.
Accompanying drawing explanation
Fig. 1: instance user situation acquisition module block diagram of the present invention;
Fig. 2: the treatment scheme sketch of the method for the invention;
Fig. 3: the treatment scheme detail drawing of the method for the invention.
Embodiment
Now in conjunction with the embodiments, the invention will be further described for accompanying drawing:
The technical solution adopted for the present invention to solve the technical problems comprises the following steps:
Step one: contextual information when recording user browses mobile webpage at every turn and the type of institute's browsing content.User often clicks a web page contents, and the type of the contextual information such as current date, time, place and click on content all will to be sent in server database and to preserve.If the data gathered are too accurate, the problem of Sparse can be brought thus make to recommend to lose efficacy, therefore in the process of initial acquisition data, needing simply to process gathered data, preserve again afterwards.
Step 2: process existing user network page browsing record data and analyze, calculate the click volume that user browses dissimilar content under different situation, click volume is higher, and user interest degree is higher.Generate user preference table, this table intuitively can express user at the web page contents when and where liking reading which kind of type.
Step 3: context aware degree calculates.
When user opens webpage at every turn, first obtain user current context by mobile terminal device, this is for user recommends user under current context to like the precondition of content.
After obtaining user's current context, carry out Similarity Measure with the situation in user preference table.Comprising the multiple point of situations such as when and where due to situation, therefore when calculating the similarity of x and y two situations, obtaining point context aware degree such as the time similarity of x and y and place similarity first respectively.Can understand by analyzing user historical data the different web pages information of factor of influence different point situation to select to(for) user, namely differently dividing the weight of situation in Similarity Measure process.The final similarity similarity (x, y) calculating two situations by context aware degree computing formula.
Divide in the computation process of context aware degree, often need to carry out extensive to situation, namely extensive process is progressively gather the process of faling apart for whole, the rule extensive according to certain, and it is exactly extensive that adjacent, close or similar data are summarized by same data the process represented.
Step 4: the result of calculation according to context aware degree reorders to user preference table from high to low, again according to click volume to content orderings different under same situation, like this, in user preference table, the web page contents type of Section 1 is then the content that user is most interested under current context.Meanwhile, filter out the data repeated in content type in table, what finally obtain is then the sequence of user preference under current context.According to the sequence of user preferences content type under current context, then can be user and recommend its interested content.
Specific embodiment:
Example is recommended as to be realized by smart mobile phone browsing based on the webpage personalization of context aware, in this example, user context is made up of date, when and where, as Fig. 1 represents, date and time can obtain respectively by the calendar of mobile terminal and clock, and user locations initiatively can input position information to obtain by acquisition for mobile terminal gps data or by user.
The treatment scheme of the personalized recommend method browsed of the mobile webpage based on context aware of the present invention as shown in Figure 2.Specific implementation step is as follows:
Step 201: obtain user context and Web browsing history data.
Contextual information when recording user browses mobile webpage at every turn and the type of institute's browsing content.In this example, situation comprises date, when and where.With 1-7, date represents that Monday is to Sunday respectively; Time is then one day is divided into 8 time periods to represent with 1-8 respectively, and 1 represents 0:00-3:00, and 2 represent 3:00-6:00, by that analogy; Place can be inputted by user and obtain, and often when a user opens the web page, point selection dialog box ejectedly, selects current site by user.Web page contents type adopts predefined method in advance, is < physical culture by the division of teaching contents in webpage, amusement, politics, military > tetra-class, and marks with label.User often clicks a web page contents, then got off by the label record of current date, time, place and institute's browsing content, and preserves in a database with following form:
R_history＝(Date,Time,Location，Conent)(1)
Wherein, Date represents the date, and Time represents the time, and Location represents place, and Content represents the content type of user's webpage clicking.
Step 202: dynamically generate user preference table.
Before each user opens webpage, all can first according to the user's browsing histories data genaration user preference table recorded before, therefore user preference table is that not timing dynamically updates.Add up user's browsing histories data, click volume Clicktimes represents that user clicks the number of times of dissimilar content under identical situation, and the number of clicks bright user that more speaks more is interested in this content, finally can obtain user preference table, be expressed as:
R_preference＝(Date，Time,Location，Conent，Clicktimes)(2)
Step 203: context aware degree calculates.
Calculate user's current context CTX
curwith the similarity of situation in user preference table.Situation in user preference table is used CTX
i(i=1,2 ..., n) represent, n represents the record number of user preference table.Calculate the similarity of situation in current context and preference table respectively, use Sim
irepresent, have:
Wherein, m represents the number of types of situation, and in this example, have date, when and where three situations, therefore m value is 3; δ
krepresent the validity of the data of corresponding different situation, because the data of gathered three situations are all effective, δ
kvalue all can be set to 1; w
krepresent weights shared by different situation.Final context aware degree computing formula is:
Step 204: interest sequence under user's current context;
According to the context aware degree Sim calculated
i(i=1,2, n) from high to low user preference table is sorted, by the click volume Clicktimes in preference table, partial ordering is carried out to same situation different content type, like this, in newly-generated user preference table, the web page contents type of Section 1 is then the content that user is most interested under current context.Meanwhile, filter out the data repeated in content type in table, what finally obtain is then the sequence of user preference under current context.According to the sequence of user preferences content type under current context, then can be user and recommend its interested one or one group of content.
Fig. 3 is the treatment scheme detail drawing of the method for the invention,
Wherein, step 203, comprises step 301 further, and 302 and 303.
Step 301: obtain user's current context.Open in the process of webpage user by mobile terminal device, need the current context being obtained user by this equipment, situation obtain manner obtains sight method with step 201.Current context CTX
curcan be expressed as:
CTX
cur＝(Date,Time,Location)(5)
Step 302: calculation date, similarity s corresponding to when and where three situations first respectively
k(CTX
cur, CTX
i), wherein k=3.Need in computation process to apply to the extensive of situation.In user's browsing histories data, by 1 to 7, the date represents that Monday is to Sunday respectively, Mon-Fri can extensively be working day, and Saturday and Sunday can extensively be weekend, and this is that one-level is extensive; On this basis, can be " arbitrary sky " by all data generaliza-tion carrying out extensive.In like manner, carry out that one-level is extensive is divided into morning, the morning, afternoon and evening the time, if once carrying out extensive, can day and night be divided into.Place then can be divided into amusement and study or indoor and outdoors according to different attribute.
In the process calculating similarity, first contrast raw data, identical, similarity is 1, different, then it is extensive to carry out one-level, contrasts again, until two data after finally extensive are identical.Often carry out once extensive, similarity will be reduced by half on original basis, if such as carry out one-level extensive after two data identical, then the similarity of raw data is 0.5, if the extensive rear data of secondary are identical, then the similarity of raw data is 0.25.
Statistical study is carried out to user's browsing histories data, determines the weight of different situation for the impact of user preference.
For the weight calculation of date Date, first calculate interest ratio, the click volume Clicktim (eConten of namely different contents
it) account for the ratio of total click volume Totalclicktime, content type comprises < physical culture, amusement, and politics, military > tetra-class, needs to calculate respectively.
I is set to 1 to 4, represents physical culture, amusement, political and military four class content successively.
Calculate the corresponding Content of situation Date
ithe variance of click volume.Date comprises 7 values, the Content that different value is corresponding
iclick volume be respectively X
1, X
2..., X
7, then variance can be tried to achieve by following formula:
Wherein
for user click on content Content every day
imean value.
The interest ratio of different content under situation Date is multiplied by corresponding variance, then can tries to achieve the interest variance V of situation Date
date:
In like manner can try to achieve the interest variance V of time situation Time and place situation Location respectively
timeand V
location.
Like this, weight shared by date situation Date can be expressed as:
In like manner can try to achieve time situation weight w
timewith place situation weight w
location.
Step 303: each context aware degree obtained in step 302 and weight thereof are brought in context aware degree computing formula, then can obtain the similarity of arbitrary situation in current context and user preference data.
Claims (3)
1., based on a mobile webpage content recommendation method for situation, it is characterized in that step is as follows:
Step 1: contextual information when recording user browses mobile webpage at every turn and the type of institute's browsing content, when user often clicks a web page contents, the contextual information of current web page and the type of click on content to be sent in server database with user network page browsing historical record and to preserve; Described contextual information is date, when and where;
Described user network page browsing historical record represents:
R_history＝(C
1,C
2,…,C
n,Conent)
Wherein, C
krepresent the different contextual information of user, k represents the number of situation, and Content is the type of the web page contents that user clicks;
Step 2: the same web page browsing history in user's browsing history is counted, represent that user clicks the number of times of dissimilar content respectively under different situation with Clicktimes, the user preference table of generation is:
R_preference＝(C
1,C
2,…,C
n,Conent,Clicktimes)；
Step 3: open in the process of webpage by mobile terminal device user, obtains the current context CTX of user by this equipment
cur=(C
1, C
2..., C
n), adopt following context aware degree computing formula to calculate the similarity of situation in current context and user preference table,
Wherein: s
k(x, y) is point situation C of situation x and y
ksimilarity s
k(x, y); δ
krepresent corresponding situation C
kthe validity of data, effectively then value is 1, invalid, and value is 0; Divide situation C
kon the weight w of user preference impact
krepresent;
Step 4: the result of calculation according to context aware degree reorders to user preference table from high to low, again according to click volume to content orderings different under same situation, in user preference table, the web page contents type of Section 1 is then the content most interested under current context of user; The data repeated in content type in filter table, obtain the sequence of user preference under current context, then can be user recommend related web page contents according to ranking results;
Weight w in described step 3
kcalculation procedure as follows:
Step (1): calculate interest ratio
with the click volume Clicktime (Content of different content
i) ratio that accounts for total click volume Totalclicktime is interest ratio
wherein Content
irepresent different web page contents types;
Step (2): calculate situation C
kcorresponding Content
ithe variance of click volume
Step (3): by different content at situation C
kunder interest ratio be multiplied by corresponding variance, then can try to achieve situation C
kinterest variance
Step (4): different situation C
kshared weight is
2. according to claim 1 based on the mobile webpage content recommendation method of situation, it is characterized in that: described contextual information is obtained by terminal device embedded sensors or clock.
3. according to claim 1 based on the mobile webpage content recommendation method of situation, it is characterized in that: the webpage that described user browses needs to carry out predefine, use <div></divGreatT .GreaT.GT that web page contents is divided into different semantic chunk, and mark with label, user often clicks a web page contents, Current Content generic can be obtained, i.e. Content.
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