Disclosed is method and system for generating recommendations to a user. System receives real time data associated with users for scenarios and batch data associated with multiple users from different data sources, received from different data channels. The user is online user. System pre-processes batch data and real time data to generate pre-processed data and stores preprocessed data in distributed, scalable big data store. System filters pre-processed data based on rules to obtain filtered data. System applies combination of machine learning techniques on filtered data, based on the scenarios associated with the user, leveraging inter-play between machine learning techniques, to generate personalized recommendations for individual user and storing the personalized recommendations in distributed database. Machine learning techniques are customized to work in distributed processing mode to reduce overall processing time. System recommends user with the personalized recommendations comprising products or services.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A processor-implemented method for generating recommendations, the method comprising: receiving, by one or more hardware processors, real time data associated with a user, for one or more scenarios associated with the user, and batch data associated with one or more users, from one or more data sources, received from a plurality of data channels; pre-processing, by the one or more hardware processors, the batch data and the real time data, to generate pre-processed data; storing, by the one or more hardware processors, the pre-processed data in a distributed database; filtering, by the one or more hardware processors, the preprocessed data based on one or more rules to obtain filtered data; applying, by the one or more hardware processors, a combination of a plurality of machine learning techniques on the filtered data, based on the one or more scenarios associated with the user, to leverage interplay between the plurality of machine learning techniques, in order to generate a set of personalized recommendations associated with the user individually, wherein the plurality of machine learning techniques are customized to work in a distributed processing mode. 2. The method of claim 1, wherein the one or more data sources, comprises a retailer database, a user database, a product database, and a service database, wherein the one or more data sources are received from a plurality of data channels comprising a store, mobile, web, and social media. 3. The method of claim 1, wherein the batch data comprises user history data, user activity data, user transaction details, order history, product attributes, service attributes, pricing data, Inventory data, Clickstream data, click stream logs, device data, Internet Protocol (IP) address, time stamp data, user location details and Social data. 4. The method of claim 1, further comprising: building a machine learning model for the combination of the plurality of machine learning techniques selected based on the one or more scenarios associated with the user; implementing the machine learning model so built on one or more instances of a production server to obtain a score of impact of a set of personalized recommendations generated by the machine learning model; refining the machine learning model, by modifying the combination of the plurality of machine learning techniques if the score is less than a threshold value; and implementing the combination of the plurality of machine learning techniques so modified. 5. The method of claim 4, further comprises applying the combination of the plurality of machine learning techniques of the machine learning model, if the score is more than or equal to the threshold value to generate the set of personalized recommendations. 6. The method of claim 1, further comprises recommending the online user with the set of personalized recommendations, wherein the set of personalized recommendations comprises at least one of items or services. 7. The method of claim 1, wherein the selection of the plurality of machine learning techniques based on the one or more scenarios associated with the user, and application of combination of the plurality of machine learning techniques so customized to work on a scalable big data store leverages distributed processing capability of the plurality of machine learning techniques so customized by mining big data 8. The method of claim 1, further comprises storing one or more sets of personalized recommendations for each user, corresponding to the one or more scenarios, in the distributed database, and generating recommendations by retrieving at least one of the one or more sets of personalized recommendations based on current scenario of the user and the real time data associated with the user. 9. The method of claim 1, further comprises generating recommendations comprising items or services based on the set of personalized recommendations and the real time data, when the user is an unknown user or a known user. 10. The method of claim 1, wherein the distributed database is a scalable big data store. 11. The method of claim 1, wherein the one or more scenarios are associated with activities of the user, and wherein the user is an unknown user or a known user, and wherein the one or more scenarios are further related to browsing actions by the user, and wherein the website pages comprises at least one of a home page, a search page, a product description page and an order confirmation page. 12. The method of claim 7, wherein the one or more scenarios, corresponding to the browsing actions of the user further comprise: data attributes related to a home page comprising ‘Inspired By user's Browsing History’, ‘User might also Like’, and ‘Other Chosen Items especially for the user’; data attributes related to a search page comprising ‘Users who searched the terms also viewed’, ‘User may also like’, ‘Clearance deals especially for the user’; data attributes related to an item/a service description page comprising ‘Users who viewed the same page also viewed’, ‘Users who bought the items also bought’, and ‘Frequently bought together’, and data attributes related to an order confirmation page comprising ‘Users who bought the items also bought’, ‘Handpicked for the user’, and ‘Recommendations based on the user's Interest’. 13. The method of claim 1, wherein the real time data comprises search terms used by the user, location of the user, time of the day associated with the user, current session details of the user, and an IP address of the user. 14. The method of claim 1, further comprises using Natural language Processing (NLP) based Text analytics techniques to mine user sentiments around review comments associated with products or services to further generate recommendations with positive sentiment distribution. 15. The method of claim 1, wherein filtering the pre-processed data based on the one or more rules enables a user to create the one or more rules or modify the one or more rules associated with the one or more scenarios, and wherein, the one or more rules further comprise modifiable attributes associated with items or services, a time period, threshold values for the plurality of machine learning techniques, and assign weights to the modifiable attributes comprising a margin value, one or more units sold, a sale value, and a price. 16. A system for generating recommendations, the system comprising: a processor; and a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, the plurality of modules comprising: a receiving module receives, real time data associated with a user for plurality of scenarios and batch data associated with one or more users, from one or more data sources, received from a plurality of data channels; a pre-processing module, pre-processes the batch data and the real time data to generate pre-processed data; stores the pre-processed data in a distributed database; a filtering module filters the preprocessed data based on one or more rules to obtain filtered data; a recommendation engine applies combination of a plurality of machine learning techniques on the filtered data based on the one or more scenarios, to leverage interplay between the plurality of machine learning techniques, in order to generate a set of personalized recommendations associated with the user individually, wherein the plurality of machine learning techniques are customized to work in a distributed processing mode. 17. The system of claim 16, wherein the one or more scenarios, corresponding to browsing actions of the user further comprise: data attributes related to a home page comprising ‘Inspired By user's Browsing History’, ‘User might also Like’, and ‘Other Chosen Items especially for the user’; data attributes related to a search page comprising ‘Users who searched the terms also viewed’, ‘User may also like’, ‘Clearance deals especially for the user’; data attributes related to an item/a service description page comprising ‘Users who viewed the same page also viewed’, ‘Users who bought the items also bought’, and ‘Frequently bought together’, and data attributes related to an order confirmation page comprising ‘Users who bought the items also bought’, ‘Handpicked for the user’, and ‘Recommendations based on the user's Interest’. 18. The system of claim 16, the recommendation engine, builds a machine learning model for the combination of the plurality of machine learning techniques selected based on the one or more scenarios associated with the user; implements the machine learning model so built on one or more instances of a production server to obtain a score of impact of a set of personalized recommendations generated by the machine learning model; refine the machine learning model by modifying the combination of the plurality of machine learning techniques if the score is less than a threshold value; and implement the combination of the plurality of machine learning techniques so modified. 19. The system of claim 16, wherein selection of the plurality of machine learning techniques based on the one or more scenarios associated with the user and application of combination of the plurality of machine learning techniques customized to work on a scalable big data store leverages distributed processing capability of the plurality of machine learning techniques so customized, by mining big data. 20. A non transitory computer readable medium having embodied thereon a computer program executed in a computing device for generating recommendations, the computer program comprising: a program code for receiving, real time data associated with a user for one or more scenarios and batch data associated with one or more users, from one or more data sources, received from a plurality of data channels a program code for pre-processing the batch data and the real time data to generate preprocessed data; a program code for storing the pre-processed data in a distributed database; a program code for filtering the pre-processed data based on one or more rules to obtain filtered data; a program code for applying a combination of a plurality of machine learning techniques on the filtered data based on the one or more scenarios in order to generate a set of personalized recommendations associated with the online user individually, wherein the plurality of machine learning techniques are customized to work in a distributed processing mode.