CN117909575A - Digital recommendation method and system for tourist attractions in historical urban area - Google Patents
Digital recommendation method and system for tourist attractions in historical urban area Download PDFInfo
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Abstract
The invention provides a digital recommendation method and a digital recommendation system for tourist attractions in a historical urban area, wherein the method comprises the following steps: collecting historical tourist intention data of tourists in tourist attractions of the city block to obtain the tourist intention attractions; the background information of the scenic spot of the intention of travelling is arranged to obtain background information of the scenic spot; quantitatively evaluating the background information of the scenic spot based on the preset analysis index, the preset analysis index weight and the expert scoring to obtain a scenic spot attraction evaluation result; obtaining travel data of a target user; processing the target user travel data based on the personalized recommendation algorithm to obtain a tourist personalized travel label; and obtaining the travel recommendation scheme based on the comprehensive recommendation algorithm. According to the invention, by introducing multidimensional reference indexes into tourist attractions and combining tourist intent scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized tourist labels, more comprehensive, intelligent and personalized tourist attraction recommendation service can be provided.
Description
Technical Field
The invention relates to the technical field of travel application, in particular to a digital recommendation method and system for historical urban tourist attractions.
Background
The historic urban areas have rich cultural heritage and unique landscapes, and attract a plurality of tourists. However, tourists often face too many choices in the historic urban area, and it is difficult to determine tourist attractions most suitable for their interests and needs, so that recommended advice is required for the customers.
There are a series of technical problems and challenges in the past urban tourist attraction recommendation. These problems include: ① Information multisource integration problem: the historical urban area has multiple sources of data, including geographic data, cultural heritage data, tourist behavior data and the like, and how to effectively integrate the data to provide comprehensive scenic spot information. ② Dynamic and real-time issues: attractions and characteristics of tourist attractions may vary over time and season, how to provide accurate recommendations based on real-time updated data. ③ Personalized recommendation problem: interests and needs vary among guests, and how to provide personalized scenic spot recommendations for each guest, satisfying their unique preferences. ④ Comprehensively evaluating the problems: how to comprehensively evaluate attractions, consider the attractions of various aspects including historical cultural value, environmental quality, commercial facilities, etc. to provide more comprehensive recommendations.
Therefore, how to provide accurate and effective customized recommended services for tourists in the tourist attractions of the historical urban area becomes a problem which needs to be solved by staff in the current technical field.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a digital recommendation method and system for tourist attractions in a historical urban area, so as to achieve the purpose of providing accurate and effective customized recommendation service for tourists in the tourist attractions in the historical urban area.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
The invention provides a digital recommendation method for tourist attractions in a historical urban area, which comprises the following steps:
Collecting historical tourist intention data of tourists in tourist attractions of the city block to obtain the tourist intention attractions;
The background information of the scenic spot of the intention of travelling is arranged to obtain background information of the scenic spot;
Quantitatively evaluating the background information of the scenic spot based on the preset analysis index, the preset analysis index weight and the expert scoring to obtain a scenic spot attraction evaluation result;
Acquiring target user travel data, wherein the target user travel data comprises personal preference data, historical behavior data and position data;
Processing the target user travel data based on the personalized recommendation algorithm to obtain a tourist personalized travel label;
And processing the travel intention scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized travel labels based on the comprehensive recommendation algorithm to obtain a travel recommendation scheme.
In one possible implementation, the historical travel intent data includes a mobile phone signaled guest movement trajectory and guest check-in points of interest.
In one possible implementation, the scenic spot context information includes building information, cultural heritage information, and historical event information.
In one possible implementation, the preset analysis index includes: historical environmental atmosphere, landscape environmental quality, business environmental quality, internal traffic environmental quality, cultural and historical value, interactivity, fare and cost, people stream intensity, infrastructure, acceptance rate, season and emergency.
In one possible implementation, the expert score quantitatively evaluates the scenic spot background information based on the preset analysis index and the preset analysis index.
In one possible implementation, the tourist personalized travel tag includes a scenic area of interest and scenic feature of interest, and the processing of the target user travel data based on the personalized recommendation algorithm includes:
determining a scenic spot area of interest for the target user based on the personal preference data;
The scenic spot feature of interest of the target user is determined based on the historical behavioral data.
In one possible implementation manner, before processing the tourist intent scenic spot, scenic spot background information, scenic spot attraction evaluation result and the tourist personalized tourist label based on the comprehensive recommendation algorithm to obtain the tourist recommendation scheme, the method further comprises the following steps:
judging the information quantity of the target user travel data, and processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel label when the information quantity of the target user travel data is larger than a preset threshold value;
And when the information quantity of the tour data of the target user is smaller than or equal to a preset threshold value, obtaining the personalized tour tag of the tourist based on the tour intention scenic spot.
In one possible implementation manner, the processing of the tourist intent scenic spot, scenic spot background information, scenic spot attraction evaluation result and tourist personalized tourist label based on the comprehensive recommendation algorithm to obtain a tourist recommendation scheme includes:
Obtaining scenic spot recommended values based on a preset assignment formula;
obtaining a travel recommendation scheme based on the scenic spot recommendation value;
The preset assignment formula expression is as follows:
Wherein T represents a tourist intention sight, alpha represents a tourist intention sight weight, H represents sight background information, beta represents sight background information weight, E represents a sight attraction evaluation result, gamma represents a sight attraction evaluation result weight, P represents a tourist personalized tourist label, and delta represents a tourist personalized tourist label weight.
In one possible implementation manner, the method processes the tourist intent scenic spot, scenic spot background information, scenic spot attraction evaluation result and tourist personalized tourist label based on the comprehensive recommendation algorithm to obtain a tourist recommendation scheme, and further comprises:
Judging whether the data amount used by the target user is lower than a first preset threshold value or not;
when the using data amount of the target user is lower than a first preset threshold value, the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot are the same, and the personalized tourist label weight of the tourist is 0;
When the using data amount of the target user is higher than or equal to a first preset threshold value and lower than a second preset threshold value, the weight of the tourist intention scenic spot, the background information weight of the scenic spot, the evaluation result weight of the attraction of the scenic spot and the personalized tourist label weight of the tourist are the same;
When the data amount used by the target user is larger than a second preset threshold value, the personalized tourist label weight of the tourist is equal to the sum of the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot, and the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot are the same;
Wherein, the sum of the weight of the tourist intention scenic spot, the scenic spot background information weight, the scenic spot attraction evaluation result weight and the tourist personalized tourist label weight is 1.
In order to achieve the above object, the present invention further provides a digital recommendation system for tourist attractions in a historical urban area, comprising:
the tourist intention scenic spot module is used for collecting the historical tourist intention data of tourists in the tourist scenic spot of the city block to obtain the tourist intention scenic spot;
The scenic spot background information module is used for sorting background information of scenic spots of tourist intentions to obtain scenic spot background information;
the scenic spot attraction evaluation result module is used for quantitatively evaluating scenic spot background information based on preset analysis indexes, preset analysis index weights and expert scores to obtain scenic spot attraction evaluation results;
The target user using data quantity acquisition module is used for acquiring target user travel data, wherein the target user travel data comprises personal preference data, historical behavior data and position data;
The tourist personalized travel label module is used for processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel label;
and the travel recommendation scheme generation module is used for processing the travel intention scenic spots, the scenic spot background information, the scenic spot attraction evaluation result and the tourist personalized travel labels based on the comprehensive recommendation algorithm to obtain the travel recommendation scheme.
The beneficial effects of adopting the embodiment are as follows: collecting historical tourist intention data of tourists in tourist attractions of the city block to obtain the tourist intention attractions; the background information of the scenic spot of the intention of travelling is arranged to obtain background information of the scenic spot; quantitatively evaluating the background information of the scenic spot based on the preset analysis index, the preset analysis index weight and the expert scoring to obtain a scenic spot attraction evaluation result; obtaining travel data of a target user; processing the target user travel data based on the personalized recommendation algorithm to obtain a tourist personalized travel label; and processing the travel intention scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized travel labels based on the comprehensive recommendation algorithm to obtain a travel recommendation scheme. According to the invention, by introducing multidimensional reference indexes into tourist attractions and combining tourist intent scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized tourist labels, more comprehensive, intelligent and personalized tourist attraction recommendation service can be provided.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of a method for digitally recommending historic urban tourist attractions according to the present invention;
FIG. 2 is a schematic diagram showing a relationship between user usage data and a personalized recommendation module assignment in the digital recommendation method for tourist attractions in a historical urban area provided by the invention;
FIG. 3 is a schematic diagram of a digital recommendation system for tourist attractions in a historic urban area according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor systems and/or microcontroller systems.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Fig. 1 is a flowchart of an embodiment of a method for digitally recommending tourist attractions in a historic urban area according to the present invention.
Referring to fig. 1, the invention provides a digital recommendation method for tourist attractions in a historical urban area, which comprises the following steps:
s11, collecting historical tourist intention data of tourists in tourist attractions of the city block to obtain the tourist intention attractions;
s12, sorting background information of the tourist intention scenic spot to obtain scenic spot background information;
S13, carrying out quantitative evaluation on background information of the scenic spot based on preset analysis indexes, preset analysis index weights and expert scores to obtain scenic spot attraction evaluation results;
S14, acquiring target user travel data, wherein the target user travel data comprises personal preference data, historical behavior data and position data;
s15, processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel label;
S16, processing the tourist intention scenic spot, scenic spot background information, scenic spot attraction evaluation result and the tourist personalized tourist label based on the comprehensive recommendation algorithm to obtain a tourist recommendation scheme.
The beneficial effects of adopting the embodiment are as follows: collecting historical tourist intention data of tourists in tourist attractions of the city block to obtain the tourist intention attractions; the background information of the scenic spot of the intention of travelling is arranged to obtain background information of the scenic spot; quantitatively evaluating the background information of the scenic spot based on the preset analysis index, the preset analysis index weight and the expert scoring to obtain a scenic spot attraction evaluation result; obtaining travel data of a target user; processing the target user travel data based on the personalized recommendation algorithm to obtain a tourist personalized travel label; and processing the travel intention scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized travel labels based on the comprehensive recommendation algorithm to obtain a travel recommendation scheme. According to the invention, by introducing multidimensional reference indexes into tourist attractions and combining tourist intent scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized tourist labels, more comprehensive, intelligent and personalized tourist attraction recommendation service can be provided.
The invention relates to a traditional method for recommending tourist attractions in a plane-space angle and a time dimension by providing a dynamic digital recommending method for the tourist attractions in a historical urban area based on urban multi-mode data, so as to form dynamic optimization of urban scenic spot recommendation. The embodiment can scientifically and reasonably plan, design and adjust tourist attractions of the urban historical urban area based on a digital method, urban multi-source big data and various evaluation methods, and provides more comprehensive and intelligent scenic spot recommendation service for target users.
In one embodiment, the historical travel intent data includes a guest movement track and guest check-in points of interest signaled by the cell phone. The scenic spots most commonly accessed by tourists in the historical urban area are captured through collecting historical intention data, and scenic spot recommendation is performed based on real-time data, wherein the historical intention data comprises, but is not limited to, information such as tourist movement tracks, social media check-in POIs and the like based on mobile phone signaling. The historical intent data is updated periodically to reflect the latest condition of the attraction.
In one embodiment, the scenic spot context information includes building information, cultural heritage information, and historical event information. Scenic spot background information belongs to static input data in the embodiment, so that tourists can have sufficient historical cultural knowledge when visiting scenic spots.
In one embodiment, the preset analysis index includes: historical environmental atmosphere, landscape environmental quality, business environmental quality, internal traffic environmental quality, cultural and historical value, interactivity, fare and cost, people stream intensity, infrastructure, acceptance rate, season and emergency.
The preset analysis index of the embodiment can comprehensively evaluate the background information model from the aspects of the richness of the historical culture resources, the importance of the historical culture resources, the accessibility of the historical culture resources and the like, so that the accuracy of an evaluation system is improved.
Further, the description of the preset analysis index is described in the following table:
Based on the indexes, a mathematical model can be designed to comprehensively score the quality of the scenic spot, the scenic spot score is set as S, N indexes are all provided, the score for each indicator was S i (i=1, 2, once again, N), once again, N).
The attraction score S may be expressed as:
S= Σ (W i*Si) (i=1 to N)
In the above formula, the finally calculated S value represents the total score of the scenery spot, i.e. the scenery spot attraction evaluation result.
In one embodiment, the expert score quantitatively evaluates the scenic spot background information based on the preset analysis index and the preset analysis index. In order to evaluate tourist attractions more objectively and comprehensively, an expert scoring mechanism is introduced into the embodiment, and experts can perform qualitative and quantitative evaluation on the attractions according to the weight and the importance of each evaluation index system. Expert based knowledge and experience helps to ensure that the recommendation results are more accurate and reliable.
In one embodiment, a guest personalized travel tag includes a scenic area of interest and scenic features of interest, and processing target user travel data based on a personalized recommendation algorithm includes:
determining a scenic spot area of interest for the target user based on the personal preference data;
The scenic spot feature of interest of the target user is determined based on the historical behavioral data.
The personalized recommendation algorithm takes into account a variety of factors including the interests of the guest, past access records, rating preferences, real-time location (making nearby recommendations based on the user's current location), and so forth. Classifying different scenic spots, and classifying and accurately recommending tourist selection trends of each type. Through personalized recommendation, tourists can more easily find scenic spots meeting the interests and demands of the tourists, and the personalized degree of travel experience is improved.
In one embodiment, before processing the tourist intent scenic spot, scenic spot background information, scenic spot attraction evaluation result and the tourist personalized tourist label based on the comprehensive recommendation algorithm to obtain the tourist recommendation scheme, the method further comprises the following steps:
judging the information quantity of the target user travel data, and processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel label when the information quantity of the target user travel data is larger than a preset threshold value;
And when the information quantity of the tour data of the target user is smaller than or equal to a preset threshold value, obtaining the personalized tour tag of the tourist based on the tour intention scenic spot.
The embodiment flexibly selects a proper recommendation module based on the information quantity of the travel data of the target user. If more information is recorded before the client, the personalized recommendation algorithm is preferred to recommend scenic spots. This will ensure that the recommended results more meet the customer's personalized needs. If the background information of the client is less, the first-choice tour image big data and background information arrangement module is used for first-round recommendation so as to fill the defect of the tour data information quantity of the target user, and finally, the attraction evaluation result of the scenic spot is used as an auxiliary factor to ensure the attraction and the quality of the scenic spot.
In one embodiment, the processing of the travel intent attraction, the attraction background information, the attraction evaluation result and the tourist personalized travel label based on the comprehensive recommendation algorithm to obtain a travel recommendation scheme comprises the following steps:
Obtaining scenic spot recommended values based on a preset assignment formula;
obtaining a travel recommendation scheme based on the scenic spot recommendation value;
The preset assignment formula expression is as follows:
Wherein T represents a tourist intention sight, alpha represents a tourist intention sight weight, H represents sight background information, beta represents sight background information weight, E represents a sight attraction evaluation result, gamma represents a sight attraction evaluation result weight, P represents a tourist personalized tourist label, and delta represents a tourist personalized tourist label weight.
In one embodiment, the comprehensive recommendation algorithm is used for processing the tourist intent scenic spot, scenic spot background information, scenic spot attraction evaluation result and tourist personalized tourist labels to obtain a tourist recommendation scheme, and the method further comprises the following steps:
Judging whether the data amount used by the target user is lower than a first preset threshold value or not;
when the using data amount of the target user is lower than a first preset threshold value, the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot are the same, and the personalized tourist label weight of the tourist is 0;
When the using data amount of the target user is higher than or equal to a first preset threshold value and lower than a second preset threshold value, the weight of the tourist intention scenic spot, the background information weight of the scenic spot, the evaluation result weight of the attraction of the scenic spot and the personalized tourist label weight of the tourist are the same;
When the data amount used by the target user is larger than a second preset threshold value, the personalized tourist label weight of the tourist is equal to the sum of the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot, and the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot are the same;
Wherein, the sum of the weight of the tourist intention scenic spot, the scenic spot background information weight, the scenic spot attraction evaluation result weight and the tourist personalized tourist label weight is 1.
Fig. 2 is a schematic diagram of a relationship between user usage data and assignment of a personalized recommendation module in the digital recommendation method for tourist attractions in a historic urban area. Referring to fig. 2, the accuracy of personalized recommendation customization is determined by the amount of data used by the target user, when the amount of data used by the target user is small, the comprehensive recommendation strategy is prioritized, and when the amount of data used by the target user is large, the personalized customization strategy can be considered with emphasis, so that the applicability of travel recommendation is improved.
In one application scenario, the scores of the parts are as follows:
the travel intention sight comprises: a scenic spot: 1, B scenic spots: 0, C scenic spot: 1, D scenic spot: 1
Scenic spot history information: number of ancient sites: 10, number of historical cultural activities: 5, number of history events: 8
Scenic spot attraction evaluation results: user scoring: 4.5, social media comment number: 300, travel guideline recommendation index: 9
Tourist personalized travel label: history fan: 1, landscape fan: 0, cultural experienter: 1, exploratory: 1
Generating a result based on a recommendation scheme of a machine learning algorithm:
The user data are input into the trained model, and the algorithm identifies the user as a moderate user, so that the weight of the tourist intent scenic spot, the scenic spot historical information weight, the scenic spot attraction evaluation result weight and the tourist personalized tourist label weight are the same (0.25).
The machine learning model outputs a tourist attraction recommendation for the user. For example, the model may recommend attraction A and attraction C because these attractions fit the user's historical hobbies, cultural experimenters, and seekers' personalized tags and boost the personalized travel recommendation tag weight to 0.25, pushing the final version of the travel attraction recommendation to the client.
In one embodiment, to provide a richer and intuitive user experience, it is desirable to incorporate the data output form of the terminal, including image scoring, historical background information, and expert scoring results, to help the user better understand the appeal and characteristics of the recommended attractions, while providing multi-dimensional information.
1: Image scoring: by using an intuitive star grading system, a user can clearly see the evaluation of other tourists on the scenic spots, and the star grading can help the user to quickly know the overall popularity of the scenic spots.
2: Historical background information: historical buildings, cultural relics, events and the like of the scenic spots are browsed to better understand the cultural value of the scenic spots, so that the travel experience of the scenic spots is enriched.
3: Expert scoring results: the evaluation of the scenic spots by the expert according to the weight and the importance of each evaluation index system is displayed, and the user can obtain more comprehensive and authoritative scenic spot recommendation by looking at the scores of the expert.
FIG. 3 is a schematic diagram of a digital recommendation system for tourist attractions in a historic urban area according to an embodiment of the present invention.
Referring to fig. 3, the present invention provides a digital recommendation system for tourist attractions in a historic urban area, comprising:
The tourist intention scenic spot module 31 is used for collecting the historical tourist intention data of tourists in the tourist scenic spot of the city block to obtain the tourist intention scenic spot;
the scenic spot background information module 32 is configured to sort background information of the scenic spot of interest to obtain scenic spot background information;
The scenic spot attraction evaluation result module 33 is configured to quantitatively evaluate scenic spot background information based on a preset analysis index, a preset analysis index weight, and an expert score, to obtain a scenic spot attraction evaluation result;
The target user usage data amount acquisition module 34 is configured to acquire target user travel data, including personal preference data, historical behavior data, and location data;
the tourist personalized travel tag module 35 is used for processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel tag;
The tourist recommendation scheme generating module 36 is configured to process the tourist intent scenic spot, scenic spot background information, scenic spot attraction evaluation result and the tourist personalized tourist label based on the comprehensive recommendation algorithm to obtain the tourist recommendation scheme.
The beneficial effects of adopting the embodiment are as follows: the tourist intention spot module 31 collects the historical tourist intention data of tourists in the tourist spots of the city block to obtain the tourist intention spot; the scenic spot background information module 32 sorts the background information of the scenic spot of interest to obtain scenic spot background information; the scenic spot attraction evaluation result module 33 carries out quantitative evaluation on the scenic spot background information based on the preset analysis index, the preset analysis index weight and the expert score to obtain a scenic spot attraction evaluation result; the target user obtains target user travel data using the data amount acquisition module 34; the tourist personalized travel label module 35 processes the target user travel data based on the personalized recommendation algorithm to obtain a tourist personalized travel label; the travel recommendation generation module 36 processes travel intent attractions, attraction background information, attraction assessment results, and tourist personalized travel labels based on the integrated recommendation algorithm to obtain a travel recommendation. According to the invention, by introducing multidimensional reference indexes into tourist attractions and combining tourist intent scenic spots, scenic spot background information, scenic spot attraction evaluation results and tourist personalized tourist labels, more comprehensive, intelligent and personalized tourist attraction recommendation service can be provided.
The foregoing embodiment provides a digital recommendation system for a historical urban tourist attraction, which can implement the technical scheme described in the foregoing embodiment of the digital recommendation method for a historical urban tourist attraction, and the specific implementation principle of each module or unit can be based on the corresponding content in the foregoing embodiment of the digital recommendation method for a historical urban tourist attraction, which is not described herein again.
The invention provides a digital recommendation method and a digital recommendation system for historical urban tourist attractions, which are described in detail, wherein specific examples are applied to illustrate the principle and the implementation of the invention, and the description of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present invention, the present description should not be construed as limiting the present invention in summary.
Claims (10)
1. A digital recommendation method for historical urban tourist attractions is characterized by comprising the following steps:
Collecting historical tourist intention data of tourists in tourist attractions of the city block to obtain the tourist intention attractions;
the background information of the tourist intention scenic spot is arranged to obtain scenic spot background information;
Quantitatively evaluating the background information of the scenic spot based on a preset analysis index, a preset analysis index weight and expert scoring to obtain a scenic spot attraction evaluation result;
obtaining target user travel data, wherein the target user travel data comprises personal preference data, historical behavior data and position data;
processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel label;
And processing the travel intention scenic spot, the scenic spot background information, the scenic spot attraction evaluation result and the tourist personalized travel label based on a comprehensive recommendation algorithm to obtain a travel recommendation scheme.
2. The method of claim 1, wherein the historical travel intent data includes mobile phone signaled tourist trajectories and tourist check-in points of interest.
3. The method of claim 1, wherein the scenic spot background information comprises building information, cultural heritage information, and historical event information.
4. The digitized recommendation method for historic urban tourist attractions according to claim 1, wherein the preset analysis index comprises: historical environmental atmosphere, landscape environmental quality, business environmental quality, internal traffic environmental quality, cultural and historical value, interactivity, fare and cost, people stream intensity, infrastructure, acceptance rate, season and emergency.
5. The method of claim 1, wherein the expert score quantitatively evaluates the scenic spot background information based on the predetermined analysis index and the predetermined analysis index.
6. The method of claim 1, wherein the personalized tourist label comprises a scenic area of interest and scenic spot feature of interest, wherein the personalized recommendation algorithm based processing the target user tourist data comprises:
determining a scenic spot area of interest for a target user based on the personal preference data;
and determining the interesting scenic spot characteristics of the target user based on the historical behavior data.
7. The method for digitized recommendation of historical urban tourist attractions according to claim 1, further comprising, before the comprehensive recommendation algorithm is used to process the tourist intent attraction, the attraction background information, the attraction evaluation result and the tourist personalized tourist labels to obtain a tourist recommendation scheme:
Judging the information quantity of the target user travel data, and processing the target user travel data based on a personalized recommendation algorithm when the information quantity of the target user travel data is larger than a preset threshold value to obtain a tourist personalized travel label;
And when the information quantity of the target user tour data is smaller than or equal to a preset threshold value, obtaining the personalized tour tag of the tourist based on the tour intention scenic spot.
8. The method for digitized recommendation of historical urban tourist attractions according to claim 1, wherein the method for processing the tourist intent attraction, the scenic spot background information, the scenic spot attraction evaluation result and the tourist personalized tourist labels based on the comprehensive recommendation algorithm to obtain a tourist recommendation scheme comprises the following steps:
Obtaining scenic spot recommended values based on a preset assignment formula;
obtaining a travel recommendation scheme based on the scenic spot recommendation value;
The preset assignment formula expression is as follows:
Wherein T represents a tourist intention sight, alpha represents a tourist intention sight weight, H represents sight background information, beta represents sight background information weight, E represents a sight attraction evaluation result, gamma represents a sight attraction evaluation result weight, P represents a tourist personalized tourist label, and delta represents a tourist personalized tourist label weight.
9. The method for digitized recommendation of historical urban tourist attractions according to claim 8, wherein the comprehensive recommendation algorithm is used for processing tourist intent attractions, scenic background information, scenic attraction evaluation results and tourist personalized tourist labels to obtain a tourist recommendation scheme, and further comprising:
Judging whether the data amount used by the target user is lower than a first preset threshold value or not;
When the data amount used by the target user is lower than a first preset threshold value, the weight of the tourist intention scenic spot, the background information weight of the scenic spot and the attraction evaluation result weight of the scenic spot are the same, and the personalized tourist label weight of the tourist is 0;
when the amount of the target user usage data is higher than or equal to the first preset threshold value and lower than a second preset threshold value, the weight of the tourist intention scenic spot, the scenic spot background information weight, the scenic spot attraction evaluation result weight and the tourist personalized tourist label weight are the same;
When the target user usage data amount is larger than a second preset threshold value, the tourist personalized tourist label weight is equal to the sum of the tourist intent scenic spot weight, the scenic spot background information weight and the scenic spot attraction evaluation result weight, and the tourist intent scenic spot weight, the scenic spot background information weight and the scenic spot attraction evaluation result weight are the same;
The sum of the weight of the tourist intention scenic spot, the scenic spot background information weight, the scenic spot attraction evaluation result weight and the tourist personalized tourist label weight is 1.
10. A digital recommendation system for historic urban tourist attractions, comprising:
the tourist intention scenic spot module is used for collecting the historical tourist intention data of tourists in the tourist scenic spot of the city block to obtain the tourist intention scenic spot;
The scenic spot background information module is used for sorting the background information of the scenic spot of the tourist intention to obtain scenic spot background information;
The scenic spot attraction evaluation result module is used for quantitatively evaluating the scenic spot background information based on a preset analysis index, a preset analysis index weight and expert scoring to obtain a scenic spot attraction evaluation result;
The target user using data acquisition module is used for acquiring target user travel data, wherein the target user travel data comprises personal preference data, historical behavior data and position data;
The tourist personalized travel tag module is used for processing the target user travel data based on a personalized recommendation algorithm to obtain a tourist personalized travel tag;
And the travel recommendation scheme generation module is used for processing the travel intention scenic spot, the scenic spot background information, the scenic spot attraction evaluation result and the tourist personalized travel label based on a comprehensive recommendation algorithm to obtain a travel recommendation scheme.
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| CN202311778833.6A CN117909575A (en) | 2023-12-20 | 2023-12-20 | Digital recommendation method and system for tourist attractions in historical urban area |
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