CN116028712A - Hotel room intelligent recommendation system based on historical information - Google Patents
Hotel room intelligent recommendation system based on historical information Download PDFInfo
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Abstract
The invention provides a hotel room intelligent recommending system based on historical information, which comprises an information storage module, an interaction module, a hotel map module, a retrieving module and a recommending processing module, wherein the information storage module is used for storing historical data of a user, the interaction module is used for inputting booking information and displaying recommending results, the hotel map module is used for storing room information of a hotel, the retrieving module is used for retrieving a target room loaded with booking information, and the recommending processing module is used for analyzing and obtaining a finally recommended room from the target room. According to the system, each room in which a user stays historically is converted into a vector, the vector is processed based on all the vectors to obtain the feature vector, then the vector of the target room is compared with the feature vector to be recommended, and the recommendation result is more quantized and can meet the actual requirements of the user.
Description
Technical Field
The invention relates to the field of systems specially suitable for specific business departments, in particular to a hotel room intelligent recommendation system based on historical information.
Background
Booking hotels in advance before traveling is a common practice when traveling or going out, current hotel booking software often makes recommendation display based on prices, and users often cannot obtain finer room information when booking hotels, so that a finally-checked room can exist in a dissatisfied place for the users. There is a need for a more intelligent recommendation system in which the recommended rooms can meet the user's requirements.
The foregoing discussion of the background art is intended to facilitate an understanding of the present invention only. This discussion is not an admission or admission that any of the material referred to was common general knowledge.
Many intelligent recommendation systems have been developed, and through extensive searching and reference, it is found that existing recommendation systems have a system as disclosed in publication number CN109522431B, and these systems generally include a user terminal sending a push request to a server, where the push request includes an identity of a user; after receiving the push request, the server acquires a picture corresponding to the hotel; the server acquires the characteristics of the hotel in constant residence according to the picture of the hotel in constant residence; the server searches information of recommended hotels with feature similarity larger than a preset value with the user frequently-check hotels from a database, and pushes the information of the recommended hotels to the user terminal; the user terminal displays the information of the recommended hotels; however, the system recommends rooms based on picture recognition, so that pictures which can perfectly accord with users are difficult to find, and unsuitable points still exist in the finally recommended rooms.
Disclosure of Invention
The invention aims to provide a hotel room intelligent recommendation system based on historical information aiming at the defects.
The invention adopts the following technical scheme:
the hotel room intelligent recommending system based on the historical information comprises an information storage module, an interaction module, a hotel map module, a retrieval module and a recommending processing module;
the information storage module is used for storing historical data of a user, the interaction module is used for inputting reservation information and displaying recommendation results, the hotel map module is used for storing room information of a hotel, the retrieval module is used for retrieving target rooms of load reservation information, and the recommendation processing module is used for analyzing and obtaining finally recommended rooms from the target rooms;
the information stored by the information storage module is a room vector obtained by converting the room information of each check-in, the recommendation processing module processes the room vector of the user to obtain a feature vector, the recommendation processing module compares the room vector of the target room with the feature vector to obtain a difference index, and the recommendation processing module sends the room information with the minimum difference index to the interaction module for recommendation display;
further, the information storage module comprises an identity verification unit, an information processing unit and an information storage unit, wherein the identity verification unit is used for carrying out legal authentication on the identity of a user, the information processing unit is used for processing room information into room vectors, the room vectors comprise at least two feature groups, each feature group comprises at least two feature quantities, and the information storage unit is used for storing room vector data;
further, the information storage unit comprises a vector register, a queue processor and a room information register, wherein the vector register is used for storing all room vectors of each user, the room information register is used for temporarily storing newly added room use information, and the queue processor is used for sending the room use information to the information processing unit and storing the received room vectors in corresponding storage areas in the vector register;
further, the recommendation processing module comprises a user room processing unit and a target room comparison unit, wherein the user room processing unit is used for analyzing and processing historical room data of a user, and the target room comparison unit is used for comparing and processing a target room with an analysis result to obtain a recommended room;
the user room processing unit calculates the mind index P (i) of each feature group according to:
wherein V (i, j) is a value of the j-th feature quantity of the i-th feature group in the integrated vector, N (i) represents the number of feature quantities included in the i-th feature group, max (V (i, j)) represents the largest feature quantity value in the i-th feature group, and Min (V (i, j)) represents the smallest feature quantity value in the i-th feature group;
the comprehensive vector is a vector obtained by adding all room vectors of the user;
the special group in the feature vector comprises a mind feature group and a non-mind feature group, wherein the mind feature group is a feature group with a mind index larger than a threshold value in the comprehensive vector, the non-mind feature group is a feature group with a mind index not larger than the threshold value in the comprehensive vector, the largest feature quantity in the comprehensive vector in the mind feature group is set to be 1, the rest feature quantities are set to be 0, and all the feature quantities in the non-mind feature group are set to be 0;
the user room processing unit calculates a weight coefficient k (i) according to the following formula based on the mind indexes of all the feature groups:
wherein m is the number of feature groups;
further, the target room comparing unit processes the room vector and the feature vector of each target room according to the following formula to obtain a difference index Q:
where T (i, j) represents the value of the j-th feature quantity of the i-th feature group in the feature vector, and F (i, j) represents the value of the j-th feature quantity of the i-th feature group in the room vector of the target room.
The beneficial effects obtained by the invention are as follows:
the system calculates the room characteristics of user preference based on historical data, real data can reflect the actual situation of the user more than the description of direct white, the system records each room through vectors, the vectors comprise a plurality of feature groups, each feature group is used for reflecting the room characteristics, the vectors can comprise enough comprehensive room information, when the vectors are processed, the importance of each room characteristic to the user is analyzed according to the specific distribution of each feature group in the vectors, weight coefficients are obtained based on the importance calculation, and finally the degree of difference between the target room vector and the feature vector is calculated based on the weight coefficients, so that the finally recommended room can meet the actual demands of the user.
For a further understanding of the nature and the technical aspects of the present invention, reference should be made to the following detailed description of the invention and the accompanying drawings, which are provided for purposes of reference only and are not intended to limit the invention.
Drawings
FIG. 1 is a schematic diagram of the overall structural framework of the present invention;
FIG. 2 is a schematic diagram of an information storage module according to the present invention;
FIG. 3 is a schematic diagram of an authentication unit according to the present invention;
FIG. 4 is a schematic diagram of an information storage unit according to the present invention;
fig. 5 is a schematic diagram of a room recommendation flow according to the present invention.
Detailed Description
The following embodiments of the present invention are described in terms of specific examples, and those skilled in the art will appreciate the advantages and effects of the present invention from the disclosure herein. The invention is capable of other and different embodiments and its several details are capable of modification and variation in various respects, all without departing from the spirit of the present invention. The drawings of the present invention are merely schematic illustrations, and are not intended to be drawn to actual dimensions. The following embodiments will further illustrate the related art content of the present invention in detail, but the disclosure is not intended to limit the scope of the present invention.
Embodiment one.
The embodiment provides a hotel room intelligent recommending system based on historical information, which comprises an information storage module, an interaction module, a hotel map module, a retrieval module and a recommending processing module, and is combined with fig. 1;
the information storage module is used for storing historical data of a user, the interaction module is used for inputting reservation information and displaying recommendation results, the hotel map module is used for storing room information of a hotel, the retrieval module is used for retrieving target rooms of load reservation information, and the recommendation processing module is used for analyzing and obtaining finally recommended rooms from the target rooms;
the information stored by the information storage module is a room vector obtained by converting the room information of each check-in, the recommendation processing module processes the room vector of the user to obtain a feature vector, the recommendation processing module compares the room vector of the target room with the feature vector to obtain a difference index, and the recommendation processing module sends the room information with the minimum difference index to the interaction module for recommendation display;
the information storage module comprises an identity verification unit, an information processing unit and an information storage unit, wherein the identity verification unit is used for carrying out legal authentication on the identity of a user, the information processing unit is used for processing room information into room vectors, the room vectors comprise at least two feature groups, each feature group comprises at least two feature quantities, and the information storage unit is used for storing room vector data;
the information storage unit comprises a vector register, a queue processor and a room information register, wherein the vector register is used for storing all room vectors of each user, the room information register is used for temporarily storing newly added room use information, and the queue processor is used for sending the room use information to the information processing unit and storing the received room vectors in corresponding storage areas in the vector register;
the recommendation processing module comprises a user room processing unit and a target room comparison unit, wherein the user room processing unit is used for analyzing and processing historical room data of a user, and the target room comparison unit is used for comparing and processing a target room with an analysis result to obtain a recommended room;
the user room processing unit calculates the mind index P (i) of each feature group according to:
wherein V (i, j) is a value of the j-th feature quantity of the i-th feature group in the integrated vector, N (i) represents the number of feature quantities included in the i-th feature group, max (V (i, j)) represents the largest feature quantity value in the i-th feature group, and Min (V (i, j)) represents the smallest feature quantity value in the i-th feature group;
the comprehensive vector is a vector obtained by adding all room vectors of the user;
the special group in the feature vector comprises a mind feature group and a non-mind feature group, wherein the mind feature group is a feature group with a mind index larger than a threshold value in the comprehensive vector, the non-mind feature group is a feature group with a mind index not larger than the threshold value in the comprehensive vector, the largest feature quantity in the comprehensive vector in the mind feature group is set to be 1, the rest feature quantities are set to be 0, and all the feature quantities in the non-mind feature group are set to be 0;
the user room processing unit calculates a weight coefficient k (i) according to the following formula based on the mind indexes of all the feature groups:
wherein m is the number of feature groups;
the target room comparison unit processes the room vector and the feature vector of each target room according to the following formula to obtain a difference index Q:
where T (i, j) represents the value of the j-th feature quantity of the i-th feature group in the feature vector, and F (i, j) represents the value of the j-th feature quantity of the i-th feature group in the room vector of the target room.
Embodiment two.
The embodiment comprises the whole content in the first embodiment, and provides a hotel room intelligent recommendation system based on history information, which comprises an information storage module, an interaction module, a hotel map module, a retrieval module and a recommendation processing module;
the information storage module is used for storing room use history data of a user, the interaction module is used for inputting information and displaying recommendation results, the hotel map module is used for storing geographical distribution information of hotels, the search module is used for searching hotels meeting the user requirements, and the recommendation processing module determines rooms from the searched hotels to recommend based on the history data of the user;
referring to fig. 2, the information storage module includes an identity verification unit, an information processing unit and an information storage unit, where the identity verification unit is used to perform legal authentication on the identity of the user, the information processing unit is used to perform inductive processing on the stored information, and the information storage unit is used to store the processed information data;
referring to fig. 3, the identity verification unit includes a cache receiver, and a verification computing processor, where the cache receiver is configured to receive and store identity information and verification information of a user, and the verification computing processor performs computation processing on the identity information and the verification information to obtain address information, where the address information is an index address where the user performs data storage in the information storage unit, and the verification computing processor processes the identity information and the verification information according to the following formula:
wherein Ad represents an address information character string, id represents an identity information character string, ck represents a check information character string, lad represents the length of the address information character string, lid represents the length of the identity information character string, ad (i) represents a value corresponding to an ith character in the address information character string, ck (i) represents a value corresponding to an ith character in the check information character string, and j is a count value;
when the address information is obtained, if the address information is legal, the new room use data can be stored in the corresponding information storage unit or the historical room use data can be obtained from the corresponding information storage unit;
the information processing unit converts the room usage information into a room vector, the room vector comprises at least two feature groups, each feature group corresponds to one feature of the room, each feature group comprises at least two feature quantities, only one feature quantity in each feature group has a value of 1, the other feature quantities have a value of 0, for example, the orientation feature group comprises four feature quantities, namely east, south, west and north, the value of the feature quantity east is 1, the values of the feature quantities south, west and north are all 0, and the room is an eastward-oriented room;
referring to fig. 4, the information storage unit includes a vector register for storing all room vectors of each user, a queue processor for temporarily storing newly added room usage information, and a room information register for transmitting the room usage information to the information processing unit and storing the received room vectors to corresponding storage areas in the vector register;
the interaction module comprises a display device and an input device, wherein the display device is a display screen, the input device comprises a mouse or a keyboard, and the display device and the input device can be integrated into a touch control type display screen;
the interaction module can input a destination, reservation time and a travel range, the interaction module sends the input information to the retrieval module, the retrieval module determines a target hotel from the hotel map module according to the destination and the travel range, then determines a target room which is not reserved in the target hotel according to the reservation time, and the retrieval module sends room information of the target room to the recommendation processing module;
the recommendation processing module comprises a user room processing unit and a target room comparison unit, wherein the user room processing unit is used for analyzing and processing historical room data of a user, and the target room comparison unit is used for comparing and processing a target room with an analysis result to obtain a recommended room;
the user room processing unit acquires room vectors from corresponding storage areas in the vector register, and adds all the room vectors to obtain a comprehensive vector, wherein the value of the j-th feature quantity of the i-th feature group in the comprehensive vector is recorded as V (i, j);
the user room processing unit calculates the mind index P (i) of each feature group according to:
where N (i) represents the number of feature amounts contained in the i-th feature group, max (V (i, j)) represents the largest feature amount value in the i-th feature group, and Min (V (i, j)) represents the smallest feature amount value in the i-th feature group;
the user room processing unit calculates a weight coefficient k (i) according to the following formula based on the mind indexes of all the feature groups:
wherein m is the number of feature groups;
the user room processing unit constructs a feature vector, and the construction process comprises the following steps:
s1, screening out a characteristic group with a mind index larger than a threshold value;
s2, selecting the feature quantity with the largest feature quantity value from each feature group screened in the step S1 according to the comprehensive vector;
s3, assigning the feature quantity selected in the step S2 to be 1 in the feature vector;
s4, the rest characteristic quantities are assigned to be 0 in the characteristic vector;
the user room processing unit sends the feature vector and the weight coefficient to the target room comparison unit;
the target room comparison unit processes the room vector and the feature vector of each target room according to the following formula:
wherein T (i, j) represents the value of the j-th feature quantity of the i-th feature group in the feature vector, F (i, j) represents the value of the j-th feature quantity of the i-th feature group in the room vector of the target room, and Q is a difference index;
the target room comparison unit sends room information with smaller difference index to the interaction module for display;
referring to fig. 5, the process of making room recommendations by the system includes the steps of:
s21, logging in by a user, and acquiring historical data after identity verification;
s22, inputting reservation information by a user, and screening out a target room by the system;
s23, obtaining a room vector of the target room according to the information of the target room;
s24, obtaining a feature vector and a weight coefficient according to the historical data;
s25, calculating a difference index of each target room;
s26, displaying the recommended room information according to the difference index;
and S27, after formally checking in, sending the room information to an information storage module for storage.
The foregoing disclosure is only a preferred embodiment of the present invention and is not intended to limit the scope of the invention, so that all equivalent technical changes made by applying the description of the present invention and the accompanying drawings are included in the scope of the present invention, and in addition, elements in the present invention can be updated as the technology develops.
Claims (5)
1. The hotel room intelligent recommending system based on the historical information is characterized by comprising an information storage module, an interaction module, a hotel map module, a retrieval module and a recommending processing module;
the information storage module is used for storing historical data of a user, the interaction module is used for inputting reservation information and displaying recommendation results, the hotel map module is used for storing room information of a hotel, the retrieval module is used for retrieving target rooms of load reservation information, and the recommendation processing module is used for analyzing and obtaining finally recommended rooms from the target rooms;
the information stored by the information storage module is a room vector obtained by converting the room information of each check-in, the recommendation processing module processes the room vector of the user to obtain a feature vector, the recommendation processing module compares the room vector of the target room with the feature vector to obtain a difference index, and the recommendation processing module sends the room information with the minimum difference index to the interaction module for recommendation display.
2. The intelligent hotel room recommendation system based on historical information as claimed in claim 1, wherein the information storage module comprises an identity verification unit, an information processing unit and an information storage unit, the identity verification unit is used for performing legal authentication on the identity of a user, the information processing unit is used for processing room information into room vectors, the room vectors comprise at least two feature groups, each feature group comprises at least two feature quantities, and the information storage unit is used for storing room vector data.
3. The intelligent hotel room recommendation system based on history information as claimed in claim 2, wherein the information storage unit comprises a vector register for storing all room vectors for each user, a queue processor for temporarily storing newly added room usage information, and a room information register for transmitting the room usage information to the information processing unit and storing the received room vectors to corresponding storage areas in the vector register.
4. The intelligent hotel room recommending system based on historical information as set forth in claim 3, wherein the recommending processing module comprises a user room processing unit and a target room comparing unit, the user room processing unit is used for analyzing and processing historical room data of a user, and the target room comparing unit is used for comparing and processing the target room with the analysis result to obtain a recommended room;
the user room processing unit calculates the mind index P (i) of each feature group according to:
where V (i, j) is the value of the j-th feature quantity of the i-th feature group in the integrated vector, N (i) represents the number of feature quantities included in the i-th feature group, max (V (i, j)) represents the largest feature quantity value in the i-th feature group,
min (V (i, j)) represents the smallest feature value in the i-th feature group;
the comprehensive vector is a vector obtained by adding all room vectors of the user;
the special group in the feature vector comprises a mind feature group and a non-mind feature group, wherein the mind feature group is a feature group with a mind index larger than a threshold value in the comprehensive vector, the non-mind feature group is a feature group with a mind index not larger than the threshold value in the comprehensive vector, the largest feature quantity in the comprehensive vector in the mind feature group is set to be 1, the rest feature quantities are set to be 0, and all the feature quantities in the non-mind feature group are set to be 0;
the user room processing unit calculates a weight coefficient k (i) according to the following formula based on the mind indexes of all the feature groups:
where m is the number of feature groups.
5. The intelligent hotel room recommendation system based on historical information as claimed in claim 4, wherein the target room comparison unit processes the room vector and the feature vector of each target room to obtain the difference index Q according to the following formula:
where T (i, j) represents the value of the j-th feature quantity of the i-th feature group in the feature vector, and F (i, j) represents the value of the j-th feature quantity of the i-th feature group in the room vector of the target room.
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