Claims:
WE CLAIM
1. Predicting the user preferences on ecommerce sites using machine learning approach compromises of a
Clustering Unit;
Classifying Unit;
Prediction Unit
and a Resultant Database.
2. Predicting the user preferences on ecommerce sites using machine learning approach, according to claim 1 includes a clustering unit; wherein the clustering unit clusters the data collected from various sites according to the categories of products. The algorithm used is K-means clustering algorithm.
3. Predicting the user preferences on ecommerce sites using machine learning approach, according to claim 1 includes a classification unit, wherein the classification unit classifies the data using the clustered data sets as inputs.
4. Predicting the user preferences on ecommerce sites using machine learning approach, according to claim 1 includes a prediction unit; wherein the prediction unit uses predictive algorithm to provide data sets, based on which user preference can be drawn.
5. Predicting the user preferences on ecommerce sites using machine learning approach, according claim 1 includes a resultant database; wherein the resultant database stores the output of predictive unit.
, Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Ecommerce relates to the field of selling and buying products online. For this to be successful, the owners of online selling platforms will design their websites and application effectively so that the users can get access and browse the products of their interest efficiently. Though the applications and websites are keenly designed to be user-friendly, there are still disadvantages that needs to be addressed. The websites and application should be designed to display the products of user preferences using machine learning based approaches.
[0003] A number of different types of user preference detecting systems are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] US20140172652A1: A system and method is described for large-scale, automated classification of products. The system and method receive information about products, wherein such information includes one or more text metadata fields associated with each product, receives a set of categories, and automatically selects one or more categories from the set of categories to which each product belongs based upon at least one of the one or more text metadata fields associated with each product. A machine learning classifier may be used to automatically select the one or more categories to which each product belongs by operating upon a feature vector for each product derived from text metadata fields of the product description. The machine learning classifier may be trained using a set of pre-categorized product descriptions. The product-category associations generated by the system and method can be used to improve search engine results or product recommendations to consumers.
[0005] US20170278135A1: A method for a user to select merchandise online for purchase, by: (a) the user uploading an image to a computer system in a search query; (b) the computer system using image recognition software to find images similar to the uploaded image in the search query; (c) the computer system displaying to the user the images that are similar to the uploaded image, wherein the display of images is presented to the user as a webpage, and wherein the webpage address is saved as a unique URL; (d) the user selecting one of the displayed images, thereby selecting an article of merchandise corresponding thereto; and (e) the user purchasing the article of merchandise.
[0006] The proposed invention focuses on designing and implementing a framework that uses the databases of various online shopping sites to derive at conclusions and data sets that predicts the preferences of consumers. The invention is implemented suing various algorithms of machine learning techniques. The predictive data sets that are obtained as output from predictive algorithm will be used for drawing conclusions about the preferences of consumers and thus design the applications and website accordingly.
[0007] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0008] In the view of the foregoing disadvantages inherent in the known types of ecommerce monitoring systems now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved machine learning based approach to read the preferences of customers that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0009] In the view of the foregoing disadvantages inherent in the known types of techniques& systems to monitor the ecommerce sites now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved machine learning based approach to monitor the preferences of user while using the online sites for shopping which has all the advantages of the prior art and none of the disadvantages.
[0010] The main objective of the proposed invention to predict the preferences of consumers or users while using the online sites for shopping is based on machine learning approach. The proposed invention focuses on implementing a framework that studies the likings of consumers and creating a database that will contain the data regarding the products that are most liked by the consumers.
[0011] Yet another important aspect of the proposed invention is that the details of purchase of consumers are collected along with the unique ID assigned to each user. The primary key used is the consumer ID and the preference value of the particular product ID is stored against it in the form of (Consumer ID, value, Product ID). The algorithms that are used are K-means clustering algorithm and classification algorithm. The classified data sets are then fed to prediction algorithm as input and the resultant predictive data sets are stored and used for the purpose of analytics and decision making.
[0012] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0013] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BREIF DESCRIPTION OF DRAWINGS
[0014] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the Block Diagram of the Predicting the user preferences on ecommerce sites using machine learning approach, according to an embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0015] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0016] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one” and the word “plurality” means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0017] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of”, "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0018] Ecommerce are one of the most important means of shopping platform that is available during the high time of pandemic such as COVID 19. To control this highly contagious disease, government across the globe had imposed strict lockdowns and curfews. Only option that was left for people to buy their essentials, costumes, accessories was through online platforms. But the owners of the online shopping sites had confusions regarding the user preferences clearly and thereby even confused about stocking their warehouse.
[0019] The proposed invention is Predicting the user preferences on ecommerce sites using machine learning approach to support the owners of online shopping site to stock their warehouses according to the needs of the consumers. The invention is implemented using clustering and classification technique to arrive at clustered and classified data sets. the predictive algorithm will give the predictive data sets that will help users to stock their warehouse accordingly, thereby designing the website according to the user preferences as well.
[0020] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0021] Figure 1 illustrates the Block diagram of the Predicting the user preferences on ecommerce sites using machine learning approach 100. The system 100 includes the database of various online shopping sites depicted 101,102,103 and son on respectively. The database of 101,102,103 is used as input to the clustering unit 105. The clustering unit 105 clusters the data sets using the K-means algorithm. The classification of clustered data sets is done in the classification unit 106. The predictive unit 108 predicts the preferences of users and stores them on the resultant database 109.
[0022] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.