RECOMMENDATION ENGINE WITH PROFILE ANALYSIS
First Claim
1. A computer implemented system for generating recommendations corresponding to a plurality of items, said system comprising:
- a prompter accessible to a first user, wherein said prompter prompts said first user to input social network profile information of said first user, and wherein said prompter prompts said first user to input at least one item name for an item and assign a user score to said item;
a first search module operatively connected to said prompter, wherein said first search module searches for and elicits a first group of at least one review score generated by respective reviewers and corresponding to said item reviewed by said first user, and wherein said first search module searches for and elicits a second group of at least one review score generated only by at least one reviewer who generated said first group of at least one review score;
a processor operatively connected to said first search module, wherein said processor calculates a plurality of weight scores based on said user score and said first group of at least one review score, wherein said processor assigns said plurality of weight scores to said respective reviewers, and calculates an average weight corresponding to each of said respective reviewers, and wherein said processor calculates probability scores corresponding to each item linked to said second group of at least one review score;
a recommendation engine operatively connected to said processor, said recommendation engine comprising;
a generator that generates a list of recommendations comprising said at least one item name linked to said second group of at least one review score, wherein said list is generated based on at least said probability scores corresponding to each of item linked to said second group of at least one review score, wherein said generator provides said first user with access to said list of recommendations for review of items thereof, and wherein said generator iteratively regenerates said list of recommendations to include only names of said items yet to be reviewed by said first user and linked to said second group of at least one review score; and
an updating module operatively connected said generator, wherein said updating module updates at least said social network profile information of said first user with information corresponding to items reviewed by said first user;
a clustering module operatively connected to said prompter, wherein said clustering module has access to respective social networking profiles of a plurality of users, said respective social networking profiles being updated by said updating module, and wherein said clustering module segregates said respective social networking profiles of said plurality of users into a plurality of clusters based on at least a taste and preference elicited from said respective social networking profiles; and
a second search module operatively connected and cooperating with said clustering module for accessing each of said plurality of clusters, wherein said second search module searches for and identifies said at least one item name and corresponding user scores available in at least one cluster common to at least one of said plurality of users and said first user, and wherein said search module instructs said generator to iteratively regenerate said list of recommendations to include names of items identified by said second search module.
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Accused Products
Abstract
A computer implemented system and method includes a recommendation engine that provides accurate recommendations based on an accurate analysis of the tastes and preferences of a user. The recommendation engine takes into consideration the information corresponding to the tastes and preferences of the users using information gathered from social networking profiles of the user as well as the reviews previously provided by the user.
The recommendation engine collaborates this information with the review related information obtained from the reviewers in order to ascertain the recommendations that would match the preferences and tastes of the user.
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Citations
20 Claims
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1. A computer implemented system for generating recommendations corresponding to a plurality of items, said system comprising:
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a prompter accessible to a first user, wherein said prompter prompts said first user to input social network profile information of said first user, and wherein said prompter prompts said first user to input at least one item name for an item and assign a user score to said item; a first search module operatively connected to said prompter, wherein said first search module searches for and elicits a first group of at least one review score generated by respective reviewers and corresponding to said item reviewed by said first user, and wherein said first search module searches for and elicits a second group of at least one review score generated only by at least one reviewer who generated said first group of at least one review score; a processor operatively connected to said first search module, wherein said processor calculates a plurality of weight scores based on said user score and said first group of at least one review score, wherein said processor assigns said plurality of weight scores to said respective reviewers, and calculates an average weight corresponding to each of said respective reviewers, and wherein said processor calculates probability scores corresponding to each item linked to said second group of at least one review score; a recommendation engine operatively connected to said processor, said recommendation engine comprising; a generator that generates a list of recommendations comprising said at least one item name linked to said second group of at least one review score, wherein said list is generated based on at least said probability scores corresponding to each of item linked to said second group of at least one review score, wherein said generator provides said first user with access to said list of recommendations for review of items thereof, and wherein said generator iteratively regenerates said list of recommendations to include only names of said items yet to be reviewed by said first user and linked to said second group of at least one review score; and an updating module operatively connected said generator, wherein said updating module updates at least said social network profile information of said first user with information corresponding to items reviewed by said first user; a clustering module operatively connected to said prompter, wherein said clustering module has access to respective social networking profiles of a plurality of users, said respective social networking profiles being updated by said updating module, and wherein said clustering module segregates said respective social networking profiles of said plurality of users into a plurality of clusters based on at least a taste and preference elicited from said respective social networking profiles; and a second search module operatively connected and cooperating with said clustering module for accessing each of said plurality of clusters, wherein said second search module searches for and identifies said at least one item name and corresponding user scores available in at least one cluster common to at least one of said plurality of users and said first user, and wherein said search module instructs said generator to iteratively regenerate said list of recommendations to include names of items identified by said second search module. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer implemented method for generating recommendations corresponding to a plurality of items, said method comprising:
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prompting a first user to input social networking profile information of said first user; prompting said first user to input at least one item name; prompting said first user to review an item corresponding to the input item name; assigning a user score to said item; searching for and eliciting a first group of at least one review score, wherein said first group of at least one review score corresponds to said item reviewed by said first user; searching for and eliciting a second group of at least one review score, wherein said second group of at least one review score is linked to respective items and is generated only by at least one reviewer who generated said first group of at least one review score; calculating weight scores corresponding to a combination of a user score and each of the at least one review score present in said first group; assigning said weight scores to respective reviewers; calculating an average weight corresponding to each reviewer; calculating probability scores for each of said second group of at least one review score; generating a list of recommendations having items linked to said second group of at least one review score, wherein said list of recommendations is generated based on at least said probability scores corresponding to each of said items linked to said second group of at least one review score; providing said first user with access to said list for review of the items thereof; updating said social networking profile information with information corresponding to said items reviewed by said first user; accessing the updated social networking profile information of a plurality of users; segregating said social networking profile information into a plurality of clusters based on at least a taste and preference elicited from respective social networking profiles; accessing each of said plurality of clusters; searching for and identifying item names and corresponding user scores available in at least one cluster common to at least one of said plurality of users and said first user; iteratively regenerating said list of recommendations to include names of said items identified from said at least one cluster; and providing said user with access to the regenerated list. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A non-transitory program storage device readable by computer, and comprising a program of instructions executable by said computer to perform a method for generating recommendations corresponding to a plurality of items, said method comprising:
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prompting a first user to input social networking profile information of said first user; prompting said first user to input at least one item name; prompting said first user to review an item corresponding to the input item name; assigning a user score to said item; searching for and eliciting a first group of at least one review score, wherein said first group of at least one review score corresponds to said item reviewed by said first user; searching for and eliciting a second group of at least one review score, wherein said second group of at least one review score is linked to respective items and is generated only by at least one reviewer who generated said first group of at least one review score; calculating weight scores corresponding to a combination of a user score and each of the at least one review score present in said first group; assigning said weight scores to respective reviewers; calculating an average weight corresponding to each reviewer; calculating probability scores for each of said second group of at least one review score; generating a list of recommendations having items linked to said second group of at least one review score, wherein said list of recommendations is generated based on at least said probability scores corresponding to each of said items linked to said second group of at least one review score; providing said first user with access to said list for review of the items thereof; updating said social networking profile information with information corresponding to said items reviewed by said first user; accessing the updated social networking profile information of a plurality of users; segregating said social networking profile information into a plurality of clusters based on at least a taste and preference elicited from respective social networking profiles; accessing each of said plurality of clusters; searching for and identifying item names and corresponding user scores available in at least one cluster common to at least one of said plurality of users and said first user; iteratively regenerating said list of recommendations to include names of said items identified from said at least one cluster; and providing said user with access to the regenerated list. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification