CN111445308A - Tourist attraction recommendation method based on user portrait - Google Patents
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Abstract
The invention provides a tourist attraction recommendation method based on user portrait, which comprises the following steps: establishing a user portrait according to the personal information of the user and the personal historical travel information; searching relevant scenic spots and relevant amusement facilities which accord with the user portrait according to the types of the scenic spots to which the portrait belongs, and recommending according to the habit of selecting the scenic spots of adjacent users; after the user selects the destination, the path of the user trying to match the destination point of the same scenic spot is recommended, and a travel route is calculated according to the current position of the user and a mode that time and economic cost are mutually restricted for recommendation. According to the invention, the user portrait is established by analyzing the data of the travel journey of the user when the user goes out for traveling, and when the user selects to go out for traveling, the scenic spot corresponding to the user portrait can be matched for personalized recommendation through user portrait analysis, so that the scenic spot recommendation which meets the requirements of time and economic cost and meets the personalized requirements of the user is provided for the user.
Description
Technical Field
The invention relates to the field of scenic spot recommendation, in particular to a scenic spot recommendation method based on a user portrait.
Background
With the continuous improvement of the national economic level at present, the living standard of people is also improved to a great extent, and the requirements of people on life at the present stage are not only stopped at the stage of being saturated, but also pursue higher living quality. The travel outside is a good choice for people, and the pressure-relieving pillow can release working pressure to relieve mood and widen eyes. At the same time, people now have less and less demand for customized travel services. With the development of big data technology, more and more enterprises have more and more demands for accurately positioning the characteristics of a certain person, and also carry out accurate marketing and personalized solutions aiming at different demands of different people, which is also against the increase of the personalized demands of people for traveling outside at present. The conventional recommendation algorithms comprise content-based recommendation, user behavior-based recommendation, hybrid model-based recommendation, label-based recommendation and the like, and the conventional recommendation methods are not practical in operation modes with small product quantity, large user quantity and accurate matching.
Disclosure of Invention
The invention provides a tourist attraction recommendation method based on a user portrait, which aims to solve at least one technical problem.
To solve the above problem, as an aspect of the present invention, there is provided a tourist spot recommendation method based on a user portrait, including: establishing a user portrait according to the personal information of the user and the personal historical travel information; searching relevant scenic spots and relevant amusement facilities which accord with the user portrait according to the types of the scenic spots to which the portrait belongs, and recommending according to the habit of selecting the scenic spots of adjacent users; after the user selects the destination, the path of the user trying to match the destination point of the same scenic spot is recommended, and a travel route is calculated according to the current position of the user and a mode that time and economic cost are mutually restricted for recommendation.
Preferably, the method further comprises: clustering is carried out by calculating the similarity between users, and portrait groups with higher user portrait similarity in several classes are obtained.
Preferably, the personal information of the user comprises related information such as name, identification card number, age, home address, single consumption amount, academic calendar, occupation and the like; the personal historical travel information comprises the traveling season of recent years, the average number of people traveling, the consumption amount and the types of sights visited.
Preferably, the method further comprises: when the user visits a sight spot or other facility not in his portrait, the system records but does not immediately modify the user portrait data, only if the user's travel habits change to some extent over a certain time span.
Preferably, the clustering mode adopts an ant colony-like algorithm, and classifies each user in a computing mode similar to an ant colony to finish user clustering.
Preferably, the division is performed by the following steps:
step (1), initially randomly selecting a plurality of users as division reference points and establishing an initial pheromone matrix;
step (2), all users calculate the probability of selecting the datum point, and use roulette to select the datum point to which the users belong;
step (3), updating the reference points according to the division;
step (4), calculating the similarity between the user and the new reference point;
step (5), updating the pheromone matrix;
and (6) if the iteration condition is met, stopping circularly outputting the classification result if the iteration condition is met, and returning to the step (2) if the iteration condition is not met.
Preferably, the similarity calculation formula uses the cosine theorem to calculate the included angle between two users, and takes the cosine included angle between two user figures as the similarity between two users:
α＝arccosS
preferably, the pheromone update formula employs:
wherein Q is a self-set parameter, α is the similarity between the user and the reference point, and ρ is the pheromone volatilization parameter.
Preferably, the user reference point selection probability calculation formula is:
where τ is the pheromone and η is the reciprocal of the similarity of the user to the reference point.
According to the invention, the user portrait is established by analyzing the data of the travel journey of the user when the user goes out for traveling, and when the user selects to go out for traveling, the scenic spot corresponding to the user portrait can be matched for personalized recommendation through user portrait analysis, so that the scenic spot recommendation which meets the requirements of time and economic cost and meets the personalized requirements of the user is provided for the user.
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Fig. 1 schematically shows a flow chart of the present invention.
Detailed Description
The following detailed description of embodiments of the invention, but the invention can be practiced in many different ways, as defined and covered by the claims.
At present, people enjoy traveling outside to relax moods more and more in the busy work, and the traveling outside generally makes a travel plan by oneself or goes to a travel agency to participate in a fixed travel route. On one hand, the time cost is too high although the customized travel route is met, and on the other hand, if the travel agency is selected to participate in the fixed travel route, the customized travel route cannot be met. The conventional recommendation algorithms comprise content-based recommendation, user behavior-based recommendation, hybrid model-based recommendation, label-based recommendation and the like, and the conventional recommendation methods are not practical in operation modes with small product quantity, large user quantity and accurate matching.
The invention aims to provide a tourist attraction recommendation system based on a user portrait, which analyzes tourist journey data of a user when the user goes out to build the user portrait, can match the scenic spot which is in accordance with the user portrait for personalized recommendation through user portrait analysis when the user selects to go out to travel, and provides scenic spot recommendation which is in accordance with time and economic cost requirements and meets personalized requirements of the user for the user.
Step 1, user identification
The identity card identification and the face identification are adopted to identify the user identity, and the user does not need to establish a corresponding account number in a mailbox or mobile phone number registration mode and the like, so that the account safety of the user can be fully ensured. And the user can be ensured to obtain correct scenic spot recommendation.
Step 2, user portrait creation
The raw data in initializing the user representation includes basic information of the user and basic information of the user. Including but not limited to user basic information and target user travel behavior information. The basic information of the target user includes but is not limited to related information such as name, identification card number, age, home address, single consumption amount, academic calendar, occupation and the like. And the season of travel in recent years, the average number of people on travel, the amount of consumption, the types of sights visited, for example, as shown in the following table:
| name(s) | Study calendar | Identity card number | Occupation of the world | Month of frequent trip | Cultural historic site class | Scenic showplace | Natural wind and light | … |
| User 1 | This section | 61 … 00 | Teacher's teacher | 7，8 | 30％ | 20％ | 10％ | … |
| User 2 | Master's soldier | 51 … 0 | Doctor | 3，4 | 10％ | 10％ | 30％ | … |
| User 3 | Special section | 37 … 00 | Real estate agent | 10，12 | 50％ | 10％ | 20％ | … |
| … | … | … | … | … | … | … | … | … |
Step 3, user portrait modification
After the portrait is established for the user according to the existing tour data of the user, a scientific preference record is established for the tour preference of the user, but the tour habits of the user are influenced by time and other factors, so that the user portrait needs to be modified in real time according to the tour data newly generated by the user, when the tour of the user does not belong to scenic spots and other facilities in the portrait, the system records the tour habits, but the user portrait data is not modified immediately, and the user portrait data is modified only when the tour habits of the user are changed to a certain extent in a certain time span.
Step 4, user division
After the user portrait is established, clustering is carried out by calculating the similarity between users, and portrait groups with high user portrait similarity in several classes can be obtained.
Clustering mode: and classifying the users by adopting an ant colony-like algorithm in a calculation mode similar to an ant colony so as to finish user clustering.
The dividing method comprises the following steps:
1) several users are initially randomly selected as division reference points and an initial pheromone matrix is established.
2) All users calculate the probability of selecting a reference point and use roulette to select the reference point to which they belong.
3) Updating fiducial points according to partitions
4) Calculating the similarity between the user and the new reference point
5) Updating pheromone matrices
6) Whether an iteration condition is met, whether: returning to the step 2), stopping the circulation and outputting the classification result.
Similarity calculation formula: calculating the included angle between two users by adopting the cosine theorem, and taking the cosine included angle between two user figures as the similarity between the two users
α＝arccosS
Pheromone update formula:
wherein Q is a self-set parameter, α is the similarity between the user and the reference point, and ρ is the pheromone volatilization parameter
User reference point selection probability calculation formula
Where τ is pheromone, η is the reciprocal of the similarity of the user to the reference point
Step 5, matching mode of scenic spots
After the user portrait is obtained, relevant scenic spots and relevant amusement facilities which accord with the user portrait are searched according to the types of the scenic spots to which the portrait belongs, and recommendation is carried out according to the habits of adjacent users in selecting the scenic spots. And selecting the corresponding scenic spots. And sequencing the arrangement sequence of the scenic spots according to the calculated interest degree of the user for the scenic spots. If a user is rendering only the initial rendering in the system. The scenic spot recommendation method is changed into recommendation according to the popularity of scenic spots around the position where the user is located.
Step 6, tour route generation
After the user selects the destination, the path of the user trying to match the destination point of the same scenic spot is recommended, and an economical and practical travel route is calculated according to the current position of the user and a mode that the time and the economic cost are mutually restricted for recommendation.
Due to the adoption of the technical scheme, the invention has the following advantages and beneficial effects:
1) meet the individual requirements of users on the travel route
With the continuous improvement of living standard and the change of people to tourism mode and concept, people do not meet the group tour of a travel agency, are more prone to self-help tour, have higher and higher requirements on tourism, have stronger and stronger selectivity during outgoing tourism, have stronger and stronger individualized requirements on a tourism line, and related services such as the tourism line and the tourism service provided on the internet at present are almost fixed contents, so that people can not independently distribute time and independently select wanted tourist attractions, can not meet the requirements of the individualized tourism of people, plan the tourism line according to the scenic spots selected by users, and meet the requirements of people on freely selecting the scenic spots and freely distributing time.
2) Can meet the requirement of short-term trip of the user
People today often take a long and short trip and the user does not have much time to plan in the formation, which results in a poor subsequent tour experience. By analyzing and analyzing the user tour data, the preference portrait of the user tour spot is established, so that the user can be accurately recommended, the data collected by the user in the earlier stage is reduced, and the time for planning the journey is shortened.
3) Reducing user time and money waste
The user can select the scenic spot type preferred by the user through the scenic spot recommended by the system, the time for the user to search surrounding scenic spots and the time for collecting related information are reduced, the route planning time of the user can be reduced through the route recommendation generated after the scenic spot is selected, and the cost waste caused by unscientific route planning of the user in the follow-up process can be reduced
4) Stability of recommended scenic spots improved by ant colony-like algorithm
Clustering result change caused by small-amplitude data change in the traditional clustering algorithm can be avoided to a certain extent by using the ant colony algorithm, the relative stability of the clustering result is kept, and the types of scenic spots recommended to users have certain stability
Therefore, the innovation of the invention is that: (1) by creating an accurate representation of the user, the user's travel preferences can be portrayed. (2) The users are divided by introducing the ant colony idea, so that the defect that the traditional clustering algorithm is greatly influenced by a similarity calculation formula is overcome, the influence of abnormal values on clustering results is reduced, and the robustness of the algorithm is improved. (3) By modifying the selection formula in the ant colony, the ant colony algorithm adapts to the clustering process, the influence of similarity calculation formula shapes is avoided, and more clustering results can be explored. (4) Through the divided user clustering, users with high similarity are clustered together, accurate recommendation of tourist scenes and tourist routes can be carried out on a certain class of users, the individual requirements of the users are met, and the time consumed by the users on scenic spots and green lines is reduced.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A tourist attraction recommendation method based on user portrait is characterized by comprising the following steps:
establishing a user portrait according to the personal information of the user and the personal historical travel information;
searching relevant scenic spots and relevant amusement facilities which accord with the user portrait according to the types of the scenic spots to which the portrait belongs, and recommending according to the habit of selecting the scenic spots of adjacent users;
after the user selects the destination, the path of the user trying to match the destination point of the same scenic spot is recommended, and a travel route is calculated according to the current position of the user and a mode that time and economic cost are mutually restricted for recommendation.
2. The user representation-based tourist attraction recommendation method of claim 1, wherein said method further comprises: clustering is carried out by calculating the similarity between users, and portrait groups with higher user portrait similarity in several classes are obtained.
3. The user representation-based scenic spot recommendation method of claim 1, wherein the personal information of the user includes related information such as name, identification number, age, home address, single consumption amount, academic calendar, occupation, etc.; the personal historical travel information comprises the traveling season of recent years, the average number of people traveling, the consumption amount and the types of sights visited.
4. The user representation-based tourist attraction recommendation method of claim 1, wherein said method further comprises: when the user visits a sight spot or other facility not in his portrait, the system records but does not immediately modify the user portrait data, only if the user's travel habits change to some extent over a certain time span.
5. The user portrait based tourist attraction recommendation method of claim 2, wherein the clustering manner adopts an ant colony algorithm, and each user is classified by a calculation manner similar to an ant colony to complete user clustering.
6. The user representation-based tourist attraction recommendation method of claim 5, wherein the partitioning is performed by:
step (1), initially randomly selecting a plurality of users as division reference points and establishing an initial pheromone matrix;
step (2), all users calculate the probability of selecting the datum point, and use roulette to select the datum point to which the users belong;
step (3), updating the reference points according to the division;
step (4), calculating the similarity between the user and the new reference point;
step (5), updating the pheromone matrix;
and (6) if the iteration condition is met, stopping circularly outputting the classification result if the iteration condition is met, and returning to the step (2) if the iteration condition is not met.
7. The user representation based tourist attraction recommendation method of claim 6,
the similarity calculation formula adopts a cosine theorem to calculate an included angle between two users, and the cosine included angle between two user figures is taken as the similarity between the two users:
α＝arccosS。
8. the user representation based tourist attraction recommendation method of claim 6,
the pheromone update formula adopts:
wherein Q is a self-set parameter, α is the similarity between the user and the reference point, and ρ is the pheromone volatilization parameter.
9. The user representation based tourist attraction recommendation method of claim 6,
user reference point selection probability calculation formula:
where τ is the pheromone and η is the reciprocal of the similarity of the user to the reference point.
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