CN112347351A - Hybrid recommendation method and system for scenic spots - Google Patents
Hybrid recommendation method and system for scenic spots Download PDFInfo
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- CN112347351A CN112347351A CN202011214887.6A CN202011214887A CN112347351A CN 112347351 A CN112347351 A CN 112347351A CN 202011214887 A CN202011214887 A CN 202011214887A CN 112347351 A CN112347351 A CN 112347351A
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Abstract
The invention relates to a hybrid recommendation method and a hybrid recommendation system for tourist attractions, wherein the method comprises the following steps of: obtaining recommended tourist attractions by using a user-based collaborative filtering algorithm; obtaining recommended tourist attractions by using a collaborative filtering algorithm based on articles; and selecting the tourist attractions with the highest popularity from all recommendation results to recommend to the user. The invention avoids the limitation caused by a single recommendation algorithm, and recommends tourist attractions to clients by using a plurality of recommendation algorithms in a mixed manner, and recommends tourist attractions to clients from the perspective of the clients.
Description
Technical Field
The invention belongs to the technical field of internet, and particularly relates to a hybrid recommendation method and system for scenic spots.
Background
With the continuous development of social economy, the living standard of people is also continuously improved, including shopping, tourism and the like, however, the specific selection of users is reversed to form the most complicated problem of users in the face of countless tourist attractions, and the tourist attractions are generally recommended to the users through a single recommendation algorithm at present.
However, the current single recommendation algorithm is rough and only suitable for simple recommendation, and cannot meet the diversified demands of customers for tourist attractions, so that a hybrid recommendation method is urgently needed to meet the demands of customers.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a hybrid recommendation method and system for tourist attractions.
In order to achieve the above object, a first aspect of the present invention provides a hybrid recommendation method for tourist attractions, which includes:
(1) obtaining recommended tourist attractions by using a user-based collaborative filtering algorithm;
(2) obtaining recommended tourist attractions by using a collaborative filtering algorithm based on articles;
(3) and selecting the tourist attractions with the highest popularity from all recommendation results to recommend to the user.
Optimally, in the step (1), the user-based collaborative filtering algorithm includes analyzing the evaluation of each user on the item and calculating the similarity between all users according to the evaluation of the user on the item.
Optimally, in step (1), the user-based collaborative filtering algorithm further includes selecting N users that are most similar to the current user and recommending the items that are evaluated the highest by the N users and that have not been browsed by the current user to the current user.
Optimally, in the step (2), the item-based collaborative filtering algorithm includes analyzing browsing records of each user on the items and obtaining similarities among all the items according to the browsing record analysis.
Optimally, in the step (2), the item-based collaborative filtering algorithm further includes, for the items with high evaluation of the current user, finding the M items with the highest similarity to the items, and recommending the M items to the user.
According to another aspect of the present invention, there is provided a hybrid recommendation system for tourist attractions comprising:
a user-based collaborative filtering module: for executing a user-based collaborative filtering algorithm;
an item-based collaborative filtering module: for executing an item-based collaborative filtering algorithm;
a popularity-based recommendation module: and the method is used for recommending the tourist attractions with the highest popularity in all recommendation results to the user.
Due to the application of the technical scheme, compared with the prior art, the invention has the following advantages: according to the method, firstly, a recommended tourist attraction is obtained by using a user-based collaborative filtering algorithm, then the recommended tourist attraction is obtained by using an article-based collaborative filtering algorithm, and finally the tourist attraction with the highest popularity is selected from all recommendation results and recommended to the user. The invention avoids the limitation caused by a single recommendation algorithm, and recommends tourist attractions to clients by using a plurality of recommendation algorithms in a mixed manner, and recommends tourist attractions to clients from the perspective of the clients.
Drawings
FIG. 1 is a flow chart of a hybrid recommendation method for tourist attractions of the present invention;
FIG. 2 is a schematic structural diagram of a hybrid recommendation system for tourist attractions of the present invention;
FIG. 3 is a flow diagram of a user-based collaborative filtering module according to the present invention;
FIG. 4 is a flow diagram of an article-based collaborative filtering module of the present invention.
Detailed Description
The invention will be further described with reference to examples of embodiments shown in the drawings to which the invention is attached.
As shown in FIG. 1, the hybrid recommendation method for tourist attractions of the present invention includes the following steps:
step 1: obtaining recommended tourist attractions by using a user-based collaborative filtering algorithm;
optionally, as shown in fig. 3, the method is a flowchart based on the user collaborative filtering module, and analyzes the evaluation of each user on the articles (which tourist attractions the user has visited before and records the evaluation of the user on the tourist attractions; in this embodiment, the method further includes browsing records, ticket purchasing records, and the like on the tourist attractions); calculating similarity among all users according to the evaluation of the users on the articles (classifying the evaluation of the users according to different tourist attractions, and classifying the evaluation of the users according to good evaluation, medium evaluation and poor evaluation); selecting the N users most similar to the current user (in this embodiment, the N users most similar to the current user are selected from the users who are scored well); recommending the items which are evaluated by the N users most and not browsed by the current user to the current user (selecting the tourist attractions which are not browsed by the current user from the tourist attractions with the most evaluated N users and recommending the tourist attractions to the user).
Step 2: obtaining recommended tourist attractions by using a collaborative filtering algorithm based on articles;
optionally, as shown in fig. 4, the flowchart based on the item collaborative filtering module is used to analyze browsing records of each user on the item (which tourist attractions the user has visited and the browsed records are analyzed); analyzing according to the browsing records to obtain the similarity among all the objects (analyzing according to the browsed tourist attractions to obtain the similarity of all the tourist attractions); for the items with high evaluation of the current user, finding out M items with the highest similarity (according to the tourist attractions with high evaluation of the current user, finding out M scenic spots with the highest similarity); the M items are recommended to the user.
And step 3: and selecting the tourist attractions with the highest popularity from all recommendation results to recommend to the user.
Optionally, two groups of better tourist attractions are obtained through the two algorithms respectively, and then the current user is recommended to all the tourist attractions according to the popularity.
As shown in fig. 2, the hybrid recommendation system for tourist spots of the present embodiment includes:
a user-based collaborative filtering module: for executing a user-based collaborative filtering algorithm;
an item-based collaborative filtering module: for executing an item-based collaborative filtering algorithm;
a popularity-based recommendation module: and the method is used for recommending the tourist attractions with the highest popularity in all recommendation results to the user.
It can be seen from the above embodiments that, by using the method and system for implementing the tourism welfare, limitations caused by a single recommendation algorithm are avoided, a plurality of recommendation algorithms are used for recommending scenic spots to the user in a mixed manner, and from the perspective of the user, more attentive scenic spots are recommended to the client.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (6)
1. A hybrid recommendation method for tourist attractions is characterized by comprising the following steps:
(1) obtaining recommended tourist attractions by using a user-based collaborative filtering algorithm;
(2) obtaining recommended tourist attractions by using a collaborative filtering algorithm based on articles;
(3) and selecting the tourist attractions with the highest popularity from all recommendation results to recommend to the user.
2. The hybrid recommendation method for tourist attractions of claim 1, wherein:
in the step (1), the collaborative filtering algorithm based on the users comprises the steps of analyzing the evaluation of each user on the articles and calculating the similarity between all the users according to the evaluation of the users on the articles.
3. A hybrid recommendation method for tourist attractions according to claim 1 or 2 wherein:
in the step (1), the user-based collaborative filtering algorithm further includes selecting N users that are most similar to the current user and recommending the items that are evaluated the highest by the N users and that have not been browsed by the current user to the current user.
4. The hybrid recommendation method for tourist attractions of claim 1, wherein:
in the step (2), the collaborative filtering algorithm based on the articles comprises analyzing browsing records of each user on the articles and analyzing according to the browsing records to obtain the similarity among all the articles.
5. A hybrid recommendation method for tourist attractions according to claim 1 or 4 wherein:
in the step (2), the collaborative filtering algorithm based on the articles further includes finding the M articles with the highest similarity to the articles with the high evaluation of the current user, and recommending the M articles to the user.
6. A hybrid recommendation system for tourist attractions is characterized in that: it includes:
a user-based collaborative filtering module: for executing a user-based collaborative filtering algorithm;
an item-based collaborative filtering module: for executing an item-based collaborative filtering algorithm;
a popularity-based recommendation module: and the method is used for recommending the tourist attractions with the highest popularity in all recommendation results to the user.
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| CN202011214887.6A CN112347351A (en) | 2020-11-04 | 2020-11-04 | Hybrid recommendation method and system for scenic spots |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113111266A (en) * | 2021-04-28 | 2021-07-13 | 前海七剑科技(深圳)有限公司 | Destination recommendation method and device and computer-readable storage medium |
Citations (2)
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| CN108415928A (en) * | 2018-01-18 | 2018-08-17 | 郝宁宁 | A kind of book recommendation method and system based on weighted blend k- nearest neighbor algorithms |
| CN109862431A (en) * | 2019-01-23 | 2019-06-07 | 重庆第二师范学院 | A kind of TV programme mixed recommendation method based on MCL-HCF algorithm |
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- 2020-11-04 CN CN202011214887.6A patent/CN112347351A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108415928A (en) * | 2018-01-18 | 2018-08-17 | 郝宁宁 | A kind of book recommendation method and system based on weighted blend k- nearest neighbor algorithms |
| CN109862431A (en) * | 2019-01-23 | 2019-06-07 | 重庆第二师范学院 | A kind of TV programme mixed recommendation method based on MCL-HCF algorithm |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113111266A (en) * | 2021-04-28 | 2021-07-13 | 前海七剑科技(深圳)有限公司 | Destination recommendation method and device and computer-readable storage medium |
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