FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
TITLE: “METHOD AND SYSTEM FOR GENERATING REAL ESTATE
RECOMMENDATIONS”
Name and Address of the Applicant:
(a) Name : HDFC DEVELOPERS LIMITED
(b) Nationality: INDIAN
(c) Address : 4TH FLOOR, RAMON HOUSE, H.T. PARIKH MARG, 169 BACKBAY
RECLAMATION, CHURCHGATE, MUMBAI - 400020
The following specification particularly describes the invention and the manner in which it is to be performed.

FIELD OF THE INVENTION:
[0001] The present invention generally relates to real estate and more particularly to a method and system for generating real estate recommendations.
BACKGROUND TO THE INVENTION:
[0002] Currently, search for real estate listings on various platforms is time consuming process and a buyer may not be confident of having found the most suitable real estate based on their requirement. Key challenges in this domain are difficulty to find the right match, information overload of less relevant data and subsequent unavailability of relevant information. Available platforms offer results based on a filter search method which provides standardized results that might not be user specific. Customized filter search ask for explicit information and do not re customize the results as the preference of the user evolve over time. Other limitation of filter based real estate search is that an otherwise good match will be missed out by a user if the given real estate property lies outside the defined boundaries even on one or more of the search parameters. There is a need of a recommendation system in real estate domain that understands users’ unstated preferences, constraints & flexibilities; and update the same as the preferences of the user evolve over time. SUMMARY OF THE INVENTION:
[0003] This summary is provided to introduce a selection of concepts in a simplified format that are further described in the detailed description of the invention. This summary is not intended to identify key or essential inventive concepts of the subject matter, nor is it intended for determining the scope of the invention.
[0004] An example of a method of generating real estate recommendations includes determining, by a real estate recommendation system, login of a user on a website maintained by the real estate recommendation system. The method also includes extracting, by the real estate recommendation system, historical data associated with the user to determine user preferences. The historical data includes at least one of real estate search data and website

navigation data. Moreover, the method includes determining, by the real estate recommendation system, one or more real estate recommendations from a list of real estate listings for being displayed to the user based on the historical data. A real estate listing is associated with a plurality of attributes.
[0005] An example of a real estate recommendation system includes a communication interface in electronic communication with at least one user device. The real estate recommendation system also includes a memory that stores instructions. The real estate recommendation system further includes a processor responsive to the instructions to determine login of a user on a website maintained by the real estate recommendation system. The processor is also responsive to the instructions to extract historical data associated with the user to determine user preferences. The historical data includes at least one of real estate search data and website navigation data. The processor is responsive to the instructions to determine one or more real estate recommendations from a list of real estate listings for being displayed to the user based on the historical data.
[0006] The real estate recommendation system understands users stated and unstated preferences, their flexibilities and constraints across various real estate attributes. It aids the discovery or true preferences of the user. The real estate recommendation system shortlist real estate listings such that there are no fixed boundaries for any attribute i.e. it has the capabilities to find out the best possible result even if it lies outside the stated boundaries for one or more of the attributes. Further, the real estate recommendation system of the present invention gives recommendations in real time utilizing advanced machine learning techniques.
[0007] To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying figures.

BRIEF DESCRIPTION OF THE FIGURES:
[0008] The invention will be described and explained with additional specificity and detail with the accompanying figures in which:
[0010] FIG. 1 illustrates a block diagram of an environment, in accordance with an embodiment;
[0011] FIG. 2 illustrates an example flow diagram of a method for generating real estate recommendations, in accordance with an embodiment; and
[0012] FIG. 3 illustrates a block diagram of an electronic device, in accordance with one embodiment.
[0013] Further, skilled artisans will appreciate that elements in the figures are illustrated for simplicity and may not have been necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the figures with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
DESCRIPTION OF THE INVENTION:
[0014] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

[0015] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
[0016] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components. Appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
[0018] Embodiments of the present invention will be described below in detail with reference to the accompanying figures.
[0019] FIG. 1 illustrates a block diagram of an environment 100, in accordance with an embodiment. The environment 100 includes a real estate recommendation system 105, a plurality of location data devices, for example a location data device 110 and a location data device 115, a cloud platform 120, a plurality of user devices, for example a user device 125 and a user device 130, and a network 135.
[0020] The location data device 110 and the location data device 115 communicates

directly with the cloud platform 120 and indirectly with the real estate recommendation system 105. The location data device 110 is located in a first location corresponding to a first real estate and the location data device 115 is located in a second location corresponding to a second real estate. Herein, the term ‘real estate’ or property refers to land, buildings, and the like that a user desires to buy, sell, rent, lease, and the like. In one example, the location data device 110 and the location data device 115 are global positioning system (GPS) devices. Communication between the location data device 110, the location data device 115, the cloud platform 120, and the real estate recommendation system 105 are performed over networks. The real estate recommendation system 105 can communicate with the user device 125 and the user device 130 over the network 135. Examples of the location data device 110, the location data device 115, the user device 125, and the user device 130 include, but are not limited to, mobile phones, computers, tablets, laptops, palmtops, handheld devices, telecommunication devices, personal digital assistants (PDAs), and the like. Examples of the networks and the network 135 include, but are not limited to, a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), internet, a Small Area Network (SAN), and the like.
[0021] The location data device 110 being placed at the first location associated with the first real estate is configured to collect location data associated with the first location. In one example, the location device 110 runs a geo tagging tool in order to collect the location data of the first location. The location data can include latitude and longitude coordinates of the first location. The location data associated with the first location is further transmitted to the cloud platform 120 by the location data device 110. In one instance, the location data device 110 transmits the location data to an internet receiver through a mobile tower. The internet receiver further can transmit the location data to the cloud platform 120.
[0022] The real estate recommendation system 105 retrieves the location data of the first location from the cloud platform 120. The real estate recommendation system 105 further generates a plurality of real estate attributes, including the latitude and longitude coordinates, for the first location. Examples of the real estate attributes can include, but are not limited to, real estate configuration number or unique identification number, real estate name, the latitude and longitude coordinates, configuration type, built-up area, number of balconies, number of

bathrooms, price, real estate category, date of possession, amenities, and the like. In one example, the plurality of real estate attributes includes continuous variable attributes and categorical variable attributes. For instance, the continuous variable attributes include, but are not limited to, the latitude and longitude coordinates, built-up area, price, and date of possession. In another instance, the categorical variable attributes include, but are not limited to, real estate name, configuration type, number of balconies, number of bathrooms, price, real estate category, and amenities. Examples of the real estate category include, but are not limited to, an affordable category, a mid-range category, a luxury category, and an ultra-luxury category. Examples of the amenities of a real estate include, but are not limited to, garden, gymnasium, outdoor sports, swimming pool, recreational activities, parking, vastu, and the like.
[0023] Based on the real estate attributes of the first location, the real estate recommendation system 105 generates a first real estate listing and stores the first real estate listing locally. Similarly, the real estate recommendation system 105 generates and stores a second real estate listing, and the like. The first real estate listing, the second real estate listing and other similar real estate listings are collaborated to generate a list of real estate listings.
[0024] A user, for example an existing user, associated with the user device 105 performs login for a website through a web browser on the user device 125. The website is maintained by the real estate recommendation system 105 The real estate recommendation system 105 determines that the user has logged into the website and extracts historical data associated with the user to determine user preferences of the user. The historical data includes at least one of real estate search data and website navigation data. Herein, the term ‘real estate search data’ refers to search data that the user has previously inputted on the website to search for a real estate. Herein, the term ‘website navigation data’ refers to data associated with the user navigating through the website and clicking or viewing different categories within the website. The real estate recommendation system 105 determines a user preferred direction and range for each real estate attribute of the plurality of real estate attributes. The real estate recommendation system 105 also determines flexibility and one or more constraints for each real estate attribute of the plurality of real estate attributes.

[0025] The real estate recommendation system 105 further determines one or more real estate recommendations from the list of real estate listings that are stored. In one example, a recommendation (RECO) system is run during processing in order to determine the one or more real estate recommendations. The real estate recommendations are determined based on the historical data. Each real estate listing of the list of real estate listings is associated with the plurality of real estate attributes. The real estate recommendations are further displayed to the user. In one example, the one or more real estate recommendations are determined by using a data matrix to represent data.
[0026] For instance, an n x m data matrix is developed where n is a number of total properties and m is a number of total attributes. In the n x m data matrix, amenities including swimming pool, parking, and the like are represented as ‘1’ if present, otherwise ‘0’. Similarly, the real estate category is represented as ‘1’ for the affordable category, ‘2’ for the mid-range category, ‘3’ for the luxury category, and ‘4’ for the ultra-luxury category. Data normalization, for example between 0 and 1, is further performed on the data in the data matrix to ensure that magnitude of entries in a dataset of the data matrix is appropriate.
[0027] A machine learning method is further applied to the data to determine the one or more real estate recommendations. In one example, a dynamic weighted nearest neighbor search (DWNNS) system evolved from nearest neighbor search is used to determine similar real estates to a searched real estate, for example the first real estate. Herein, the term ‘nearest neighbor search’ refers to a proximity search for finding closest or similar objects, for example real estates. Closeness of the objects is typically expressed in terms of a dissimilarity function, for instance less similar the objects, larger is function values of the objects. In one example, the dissimilarity function for the real estates is expressed as a distance metric.
[0028] By using the DWNNS system to determine the one or more real estate recommendations, closeness or the dissimilarity function is modified such that the closeness in vector space is parameterized by weights of the real estate attributes, initial weights are derived from real estate domain knowledge, and the weights are dynamically adjusted based on the historical data of the user.

[0029] In one example, P0 and P1 are real estate properties with variable vector X0 and X1, where X is a set of features or the real estate attributes associated with a property P. A vector of initial weights ‘w’ is derived from real estate expertise of different attributes. Equations (1), (2), (3), and (4) associated with P0, P1, X0 and X1, and w are provided below.
P0 → P1 (1)
w = [w0, w1, w2 ……..wn] (2)
X0 = [X00, X01, X02 ……..X0n] (3)
X1 = [X10, X11, X12 ……..X1n] (4)
[0030] A weighted Euclidean distance (D) between two real estate properties P0 and P1 in a vector space can be described by the equation (5) given below.
[0031] Considering ‘m’ numbers of previous web footprints (or the historical data) of the user, θ is determined as a vector of weights for the web footprints and is determined as per equation (6). As importance of latest page visits and shortlists on the website is high, weights are performed accordingly.
[0032] The weighted Euclidean distance of equation (5) is further improved to incorporate weighted user web footprints and induce slight dynamic nature, as per equation (7).
[0033] Variability of the real estate attributes across the real estate projects visited previously (or the web footprints) is used to further induce a dynamic nature into the one or more real estate recommendations. A high variability in a real estate attribute implies that the

user is flexible for the real estate attribute whereas a low variability in the real estate attribute implies that the user has a constraint nature in regard to the real estate attribute.
[0034] Standard deviation of the real estate attribute across previous web footprints of the user is determined to be Si as per equation (8).
[0035] Further weights are prepared as a function of the standard deviation over the web footprints to dynamically generate the one or more real estate recommendations, to understand mindset and to induce intelligence into the real estate recommendation system 105. A dynamic KNN derived expression is determined as per equation (9).
[0036] When D(P0, P1, P2 Pm → Pk) for K is minimized, the real estate properties or
the one or more real estate recommendations corresponding to minimum distances will be recommended to the user.
[0037] An example method for generating the real estate recommendations is explained with reference to FIG. 2.
[0038] FIG. 2 illustrates an example flow diagram of a method 200 for generating real estate recommendations, in accordance with an embodiment. At step 205, the method 200 includes determining login of a user on a website by a real estate recommendation system, for example the real estate recommendation system 105 of FIG. 1. The website is maintained by the real estate recommendation system. The method of determining the login of the user on the website by the real estate recommendation system is already explained with reference to FIG. 1 and hence is not explained herein for sake of brevity.

[0039] At step 210, the method 200 includes extracting, by the real estate recommendation system, historical data associated with the user to determine user preferences of the user. The historical data includes at least one of real estate search data and website navigation data. A user preferred direction and range for each real estate attribute of the plurality of real estate attributes is determined by the real estate recommendation system. Flexibility and one or more constraints associated with the user are also determined by the real estate recommendation system for each real estate attribute of the plurality of real estate attributes. The method of extracting the historical data is already explained with reference to FIG. 1 and hence is not explained herein for sake of brevity.
[0040] At step 215, the method 200 includes determining, by the real estate recommendation system, one or more real estate recommendations from a list of real estate listings. The one or more real estate recommendations are determined based on the historical data. A real estate listing is associated with a plurality of real estate attributes that include location data among other real estate attributes (see, FIG. 1 description). The plurality of real estate attributes further includes continuous variable attributes and categorical variable attributes.
[0041] In some embodiments, a data matrix can be generated to represent data associated with the list of real estate listings and the plurality of real estate attributes. The data can further be normalized. The one or more real estate recommendations is subsequently determined by a machine learning method. The method of determining the one or more real estate recommendations is explained with reference to FIG. 1 and is not explained herein for sake of brevity.
[0042] In some embodiments, display of the one or more real estate recommendations to the user is initiated, for example by the real estate recommendation system.
[0043] Referring to FIG. 3, illustrates a block diagram of an electronic device 300, which is representative of a hardware environment for practicing the present invention. The electronic device 300 can include a set of instructions that can be executed to cause the electronic device

300 to perform any one or more of the methods disclosed. The electronic device 300 can operate as a standalone device or can be connected, for example using a network, to other electronic devices or peripheral devices.
[0044] In a networked deployment of the present invention, the electronic device 300 may operate in the capacity of a location data device, for example the location data device 110 or the location data device 115 of FIG. 1, a real estate recommendation system, for example the real estate recommendation system 105 of FIG. 1, a user device, for example the user device 125 or the user device 130, in a server-client user network environment, or as a peer electronic device in a peer-to-peer (or distributed) network environment. The electronic device 300 can also be implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a land-line telephone, a control system, a camera, a scanner, a facsimile machine, a printer, a pager, a personal trusted device, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single electronic device 300 is illustrated, the term "device" shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.
[0045] The electronic device 300 can include a processor 305, for example a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor 305 can be a component in a variety of systems. For example, the processor 305 can be part of a standard personal computer or a workstation. The processor 305 can be one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor 305 can implement a software program, such as code generated manually (for example, programmed).
[0046] The electronic device 300 can include a memory 310, such as a memory 310 that

can communicate via a bus 315. The memory 310 can include a main memory, a static memory, or a dynamic memory. The memory 310 can include, but is not limited to, computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to, random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one example, the memory 310 includes a cache or random access memory for the processor 305. In alternative examples, the memory 310 is separate from the processor 305, such as a cache memory of a processor, the system memory, or other memory. The memory 310 can be an external storage device or database for storing data. Examples include a hard drive, compact disc ("CD"), digital video disc ("DVD"), memory card, memory stick, floppy disc, universal serial bus ("USB") memory device, or any other device operative to store data. The memory 310 is operable to store instructions executable by the processor 305. The functions, acts or tasks illustrated in the figures or described can be performed by the programmed processor 305 executing the instructions stored in the memory 310. The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and can be performed by software, hardware, integrated circuits, firm-ware, micro-code and the like, operating alone or in combination. Likewise, processing strategies can include multiprocessing, multitasking, parallel processing and the like.
[0047] As shown, the electronic device 300 can further include a display unit 320, for example a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display 320 can act as an interface for a user to see the functioning of the processor 305, or specifically as an interface with the software stored in the memory 310 or in a drive unit 325.
[0048] Additionally, the electronic device 300 can include an input device 330 configured to allow the user to interact with any of the components of the electronic device 300. The input device 330 can include a stylus, a number pad, a keyboard, or a cursor control device, for example a mouse, or a joystick, touch screen display, remote control or any other device

operative to interact with the electronic device 300.
[0049] The electronic device 300 can also include the drive unit 325. The drive unit 325 can include a computer-readable medium 335 in which one or more sets of instructions 340, for example software, can be embedded. Further, the instructions 340 can embody one or more of the methods or logic as described. In a particular example, the instructions 340 can reside completely, or at least partially, within the memory 310 or within the processor 305 during execution by the electronic device 300. The memory 310 and the processor 305 can also include computer-readable media as discussed above.
[0050] The present invention contemplates a computer-readable medium that includes instructions 340 or receives and executes the instructions 340 responsive to a propagated signal so that a device connected to a network 345 can communicate voice, video, audio, images or any other data over the network 345. Further, the instructions 345 can be transmitted or received over the network 345 via a communication port or communication interface 350 or using the bus 315. The communication interface 350 can be a part of the processor 305 or can be a separate component. The communication interface 350 can be created in software or can be a physical connection in hardware. The communication interface 350 can be configured to connect with the network 345, external media, the display 320, or any other components in the electronic device 300 or combinations thereof. The connection with the network 345 can be a physical connection, such as a wired Ethernet connection or can be established wirelessly as discussed later. Likewise, the additional connections with other components of the electronic device 300 can be physical connections or can be established wirelessly. The network 345 can alternatively be directly connected to the bus 315.
[0051] The network 345 can include wired networks, wireless networks, Ethernet AVB networks, or combinations thereof. The wireless network can include a cellular telephone network, an 802.11, 802.16, 802.20, 802.1Q or WiMax network. Further, the network 345 can be a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and can utilize a variety of networking protocols now available or later developed including, but not limited to TCP/IP based networking protocols.

[0052] In an alternative example, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays and other hardware devices, can be constructed to implement various parts of the electronic device 300.
[0053] One or more examples described can implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.
[0054] The system described can be implemented by software programs executable by an electronic device. Further, in a non-limited example, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual electronic device processing can be constructed to implement various parts of the system.
[0055] The system is not limited to operation with any particular standards and protocols. For example, standards for Internet and other packet switched network transmission (for example, TCP/IP, UDP/IP, HTML, HTTP) can be used. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same or similar functions as those disclosed are considered equivalents thereof.
[0056] Various embodiments disclosed herein provide numerous advantages by providing a method and system for generating real estate recommendations. The present invention enables a user to find relevant real estates on a website maintained by a real estate recommendation system with minimal effort. The present invention suggests the relevant real estates that are likely to be shortlisted and liked by the user based on historical data associated with the user. The user need not provide any information and hence user experience is enhanced. The present invention understands unstated preferences and dynamically updates

change in preferences without asking the user. The present invention further understands dynamically learning flexibilities and constraints of user. The present invention understands fuzzy boundaries in which suitable real estates that lie outside of stated boundaries of the user but matches unstated preferences are recommended to the user. Hence, the user is provided with relevant real estate recommendations that reduce information overload on the website.
[0057] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0058] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

WE CLAIM:
1. A method of generating real estate recommendations, the method comprising:
determining, by a real estate recommendation system, login of a user on a website
maintained by the real estate recommendation system;
extracting, by the real estate recommendation system, historical data associated with the user to determine user preferences of the user, the historical data comprising at least one of real estate search data and website navigation data; and
determining, by the real estate recommendation system, one or more real estate recommendations from a list of real estate listings for being displayed to the user based on the historical data, a real estate listing of the list of real estate listings associated with a plurality of real estate attributes.
2. The method as claimed in claim 1, wherein extracting the historical data associated with
the user to determine user preferences of the user comprises:
determining, by the real estate recommendation system, a user preferred direction and range for each real estate attribute of the plurality of real estate attributes.
3. The method as claimed in claim 2, wherein extracting the historical data associated with
the user to determine user preferences of the user further comprises:
determining, by the real estate recommendation system, flexibility and one or more constraints associated with the user for each real estate attribute of the plurality of real estate attributes.
4. The method as claimed in claim 3, wherein the plurality of real estate attributes comprises a location data of a real estate associated with the real estate listing.
5. The method as claimed in claim 4, wherein the plurality of real estate attributes comprises continuous variable attributes and categorical variable attributes.
6. The method as claimed in claim 5, wherein determining the one or more real estate

recommendations comprises:
generating, by the real estate recommendation system, a data matrix to represent data associated with the list of real estate listings and the plurality of real estate attributes.
7. The method as claimed in claim 6 and further comprising:
normalising, by the real estate recommendation system, the data.
8. The method as claimed in claim 7, wherein the one or more real estate recommendations is determined by a machine learning method.
9. The method as claimed in claim 1 and further comprising:
initiating, by the real estate recommendation system, display of the one or more real estate recommendations to the user.
10. A real estate recommendation system for generating real estate recommendations, the
vendor lead management system comprising:
a communication interface in electronic communication with at least one user device of a user;
a memory that stores instructions; and a processor responsive to the instructions to:
determine login of a user on a website maintained by the real estate recommendation system;
extract historical data associated with the user to determine user preferences of the user, the historical data comprising at least one of real estate search data and website navigation data; and
determine one or more real estate recommendations from a list of real estate listings for being displayed to the user based on the historical data, a real estate listing of the list of real estate listings associated with a plurality of real estate attributes.

11. The real estate recommendation system as claimed in claim 10, wherein the processor
is further configured to:
determine a user preferred direction and range for each real estate attribute of the plurality of real estate attributes.
12. The real estate recommendation system as claimed in claim 11, wherein the processor
is further configured to:
determine flexibility and one or more constraints associated with the user for each real estate attribute of the plurality of real estate attributes.
13. The real estate recommendation system as claimed in claim 12, wherein the plurality of real estate attributes comprises a location data of a real estate associated with the real estate listing.
14. The real estate recommendation system as claimed in claim 13, wherein the plurality of real estate attributes comprises continuous variable attributes and categorical variable attributes.
15. The real estate recommendation system as claimed in claim 14, wherein the processor is further configured to:
generate a data matrix to represent data associated with the list of real estate listings and the plurality of real estate attributes.
16. The real estate recommendation system as claimed in claim 15, wherein the processor is further configured to normalize the data.
17. The real estate recommendation system as claimed in claim 16, wherein the one or more real estate recommendations is determined by a machine learning method.

18. The real estate recommendation system as claimed in claim 17, wherein the processor is further configured to:
initiate display of the one or more real estate recommendations to the user.