CN100581227C - Collaborative filtered recommendation method introducing hotness degree weight of program - Google Patents
Collaborative filtered recommendation method introducing hotness degree weight of program Download PDFInfo
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- CN100581227C CN100581227C CN200810037498A CN200810037498A CN100581227C CN 100581227 C CN100581227 C CN 100581227C CN 200810037498 A CN200810037498 A CN 200810037498A CN 200810037498 A CN200810037498 A CN 200810037498A CN 100581227 C CN100581227 C CN 100581227C
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Abstract
The invention discloses a collaborative filtering recommendation method for introducing program popularity weighting, which is characterized in that on the interface of an IPTV program, a visual menu for users to give marks is provided and a program recommendation list is made for target users according to user watching time, conduct operation and program marking data sent by a terminal set-top box. The invention comprises the detailed steps of: collecting the behavior characteristic information of users, working out a 'user-item' scoring matrix A(m, n), calculating popularity weight value, calculating similarity degree and sorting, making forecast score for the target users and sorting, and working out the recommendation list for the target users. Compared with the prior art, the method disclosed by the invention is more in accordance with objective reality, improves the quality of collaborative filtering and the precision degree of recommendation, initiatively cuts own the programs according to user preferences and behavior characteristics, carries out personalized recommendation to the programs which the users like and realizes the purpose that 'watch the program you like whenever you want'.
Description
Technical field
The present invention relates to the IPTV personalized recommendation system, specifically a kind of collaborative filtered recommendation method of introducing hotness degree weight of program.
Background technology
So-called " information overload " and " information is isotropic " phenomenon have appearred in the sharp increase of going up information along with Internet, commending system arises at the historic moment, it can find the resource that is fit to its interest for the user according to information such as user's operation history and feedbacks, for it produces personalized recommendation.Nowadays, recommended technology has been applied in every field such as ecommerce, digital library, video display amusement.Especially IPTV field, continuous development along with the Digital Television and the communication technology, the TV programme resource is more and more abundanter, the user feels very excited for watching so many program on the one hand, on the other hand again for how finding their real favorite program to feel very worried from hundreds and thousands of programs.Collaborative filter techniques is a most successful current personalized recommendation technology, and commending system that some are more famous such as WebWatcher, GroupLens, Firefly, SELECT, LileMinds and Citeseer have adopted the method for collaborative filtering.The score data that basic thought just is based on the similar nearest-neighbors of scoring produces recommendation to the targeted customer, promptly produces recommendation list to the targeted customer according to other user's viewpoint.It is based on such hypothesis: if the user is more similar to the scoring of some projects, then they are also more similar to the scoring of other project.Its starting point is to find the one group user identical with your interest, and term is called " arest neighbors ", and the core of nearest neighbor search is to calculate two users' similarity.For example user A and user B at first need to obtain user A and all scoring items of user B, select a suitable similarity calculating method then, based on scoring item number certificate, calculate the similarity numerical value of user A and user B.Use many similarity algorithms to comprise at present, Pearson's coefficient correlation (PCC), cosine similitude and adjustment cosine similitude.From the above, the committed step of collaborative filtering is to find targeted customer's nearest-neighbors, and can find nearest-neighbors accurately be to recommend whether accurate emphasis, and the similarity of calculating more accurately between the user is that arest neighbors is chosen prerequisite accurately.During but the PCC that uses calculates at present, it is exactly that the project of the common scoring of user is made no exception, do not distinguish the popular degree of project itself, closely just can reflect higher similarity as long as marked jointly and marked, so existing personalized recommendation technology accuracy is relatively poor, is consistent not to the utmost with objective reality.
Summary of the invention
The objective of the invention is a kind of collaborative filtered recommendation method of introducing hotness degree weight of program of designing at the deficiencies in the prior art, it at first defines the popular degree of TV programme, and then calculate its hotness degree weight, and in user's calculating formula of similarity, introduce this weight, the similitude that calculates in view of the above more meets reality, the similitude that calculates is more accurate, therefore can choose targeted customer's nearest-neighbors more accurately, thereby produces more accurate recommendation.
The object of the present invention is achieved like this: a kind of collaborative filtered recommendation method of introducing hotness degree weight of program, characteristics are on the interface of IPTV program, the visual menu that provides the user to mark, and make program commending and tabulate to the targeted customer according to user's viewing time, behavior operation, program score data that terminal set top box transmits, its concrete steps are as follows:
A. collect the user interest data, make " user-project " rating matrix A (m, n);
B. make the hotness degree weight value of off-line computation of Period project;
C. the project that the current active user has been marked finds corresponding hotness degree weight value;
D. make similarity size and ordering between targeted customer a and other user;
E. K the user who chooses the similarity maximum is as its nearest-neighbors collection;
F. according to nearest-neighbors set pair targeted customer not scoring item predict the scoring and the ordering;
G. will predict that the maximum top n project of scoring makes recommendation list and give the targeted customer.
Described " user-project " rating matrix A (m is to carry out arranged with user's score information and user behavior data n), the row representative of consumer, and row representative project, the element value in the matrix is then represented the favorable rating of this row user to this list of items.
The described off-line cycle calculated once by 30 minutes, the hotness degree weight value of project be with
Calculate w
tBe hotness degree weight; P
AllPopular degree summation for all items; P
tPopular degree for the t project;
Similarity between described targeted customer a and other user is incorporated into Pearson correlation coefficient with the hotness degree weight value and calculates.
The present invention compared with prior art has and more meets objective reality, improved the quality of collaborative filtering, recommend more accurate, it is according to user's preference and behavioural characteristic, initiatively program is reduced, the user is wanted that the program of seeing carries out personalized recommendation, realized " seeing that when you want you want the TV of seeing " this target.
Description of drawings
Fig. 1 is a schematic flow sheet of the present invention
Fig. 2 is a project t hotness degree weight calculation process schematic diagram of the present invention
Embodiment
Embodiment
Consult accompanying drawing 1～2, the present invention is on the interface of IPTV program, the visual menu that provides the user to mark, and make program commending and tabulate to the targeted customer according to user's viewing time, behavior operation, program score data that terminal set top box transmits, its concrete steps are as follows:
1, data collection unit features such as the viewing time by following the tracks of the user, behavior operation in the IPTV system are obtained the information of representative of consumer interest, and will be stored in the corresponding database table.
2, above-mentioned user's behavior characteristic information is carried out the processing of initial data by system, and replaces the user to finish evaluation, then according to user's score information and user behavior data, arrangement obtain " user-project " rating matrix A (m, n), the value of scoring from 1 to r
Max(i.e. marking scope be 1-5), this matrix is stored on the recommended engine device as user interest model, the row representative of consumer, row representative project, the element value in the matrix is represented the favorable rating of this row user to this list of items, favorable rating is set to 5 grades, correspond to respectively: (1) is disliked very much, and (2) are relatively disliked, and (3) are general, (4) prefer, (5) are delithted with.If the user did not estimate certain project, in rating matrix, be set to 0 so.
3, to " user-project " rating matrix A (m n) carries out the off-line computation of Period, obtains the hotness degree weight and the storage of each project, and the off-line cycle calculated once by 30 minutes, (also can decide) according to the frequency that the user upgrades wherein, the popular degree P of project t
tBe defined as: the number of times that project t is marked, the number of nonzero term during promptly t is listed as in user-project rating matrix, P
t=| U (t) |, as seen, the number of times of being marked is many more, and project is popular more, and the number of times of being marked is few more, and project is got over unexpected winner.Its hotness degree weight w
tBe defined as:
P wherein
AllBe the popular degree summation of all items, promptly all items total degree of being marked can obtain by all nonzero term numbers in user-project rating matrix.(the hotness degree weight value of each project of employing off-line computation of Period also is recorded among the W (n) for m, the n) data in, and this value has reflected corresponding project role size when calculating user's similarity according to matrix A.For example user 1 and user 2 have estimated film " No. seven, the Changjiang river " and " I am Liu Yuejin ", but by the scanning rating matrix, we find that the number of times that " No. seven, the Changjiang river " is estimated is 200 times, be far longer than " I am Liu Yuejin " by the number of times estimated 40 times, suppose that all the scoring number of times on this film collection are 3000, so the hotness degree weight w in " No. seven, the Changjiang river "
" No. seven, the Changjiang river "=log (3000/200)=2.7, the hotness degree weight w of " I am Liu Yuejin "
" I am Liu Yuejin "=log (3000/40)=4.3, like this, " I am Liu Yuejin " role when weighing user 1 and user's 2 similarity is greater than " No. seven, the Changjiang river ".
4, when targeted customer a arrives, (m n), obtains a scoring item set T to scanning rating matrix A
a, to each project t ∈ T
a, in W (n), find corresponding w
tRecommended engine is according to " user-project " rating matrix A (m, n) and hotness degree weight, adopt the similarity of targeted customer a and other user u to calculate formation user similarity matrix Sim (m, m), in calculating, similarity introduces the hotness degree weight of common scoring item, computational methods are as follows: based on the Pearson correlation coefficient method, the hotness degree weight that wherein adds in the public scoring item each in the molecule, for similarity being limited between-1～1, the hotness degree weight maximum that adds public scoring item in the denominator is done divisor, and formula is as follows:
By computing formula as can be known, as the popular degree P of project t
tBig more, hotness degree weight w
tMore little, this weight joins in the calculating formula of similarity, and the similarity that obtains is just more little.Otherwise, as the popular degree P of t
tMore little, the similarity that obtains is just big more, therefore meets this general knowledge.Watch the high more film of popular degree jointly, the user's similitude that reflects is low more, otherwise, watch the low more film of popular degree jointly, the user's similitude that reflects is just high more.The result sorts from high to low with aforementioned calculation.
5, according to size of the similarity between above-mentioned targeted customer a and other user and ordering, find preceding k the nearest-neighbors the most similar, form nearest-neighbors collection [knn to targeted customer a
1, knn
2, L, knn
k], make sim (a, knn
1)＞sim (a, knn
2)＞L＞sim (a, knn
k).
6, scan A (m, n), the project set T that finds user a not mark
a', at each project j that does not mark of active user a, predictive user a adopts following formula to each t ∈ T to the scoring of project j
a' calculate and predict score value;
The user a that calculates is to all not prediction scorings of scoring item, and according to sorting from big to small, the top n item design recommendation list RecList (N) that chooses the score value maximum gives current active user a with it.
The present invention compares with existing collaborative filtered recommendation method, in similarity is calculated, considered the popular degree difference of project itself, be reflected in the computing formula, made result of calculation more meet objective reality as weight, to recommending accuracy to make moderate progress, improved the recommendation quality to a certain extent.
Claims (4)
1, a kind of collaborative filtered recommendation method of introducing hotness degree weight of program, it is characterized in that on the interface of IPTV program, the visual menu that provides the user to mark, and make program commending and tabulate to the targeted customer according to user's viewing time, behavior operation, program score data that terminal set top box transmits, its concrete steps are as follows:
A. collect the user interest data, make " user-project " rating matrix A (m, n);
B. make the hotness degree weight value of off-line computation of Period project;
C. the project that the current active user has been marked finds corresponding hotness degree weight value;
D. introducing the project that the current active user has been marked finds corresponding hotness degree weight value to calculate the similarity size between targeted customer and other user and sort;
E. K the user who chooses the similarity maximum is as its nearest-neighbors collection;
F. according to nearest-neighbors set pair targeted customer not scoring item predict the scoring and the ordering;
G. will predict that the maximum top n project of scoring makes recommendation list and give the targeted customer.
2, according to the collaborative filtered recommendation method of the described introducing hotness degree weight of program of claim 1, it is characterized in that described " user-project " rating matrix A (m, n) be to carry out arranged with user's score information and user behavior data, the row representative of consumer, row representative project, the element value in the matrix are then represented the favorable rating of this row user to this list of items.
3, according to the collaborative filtered recommendation method of the described introducing hotness degree weight of program of claim 1, it is characterized in that the described off-line cycle calculated once by 30 minutes, the hotness degree weight value of project be with
Calculate w
tBe hotness degree weight; P
AllPopular degree summation for all items; P
tPopular degree for the t project.
4,, it is characterized in that the similarity between described targeted customer and other user is incorporated into Pearson correlation coefficient calculating with the hotness degree weight value according to the collaborative filtered recommendation method of the described introducing hotness degree weight of program of claim 1.
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