Systems and methods for providing recommendations based on collaborative and/or content-based nodal interrelationships
First Claim
1. A method comprising:
- scanning, at at least one server, a network of data sources, thereby identifying item data for a plurality of items and review data from a plurality of reviewers for a plurality of the items;
prioritizing, at the at least one server, the data sources based in part on the strength of correlations between the data sources and predictive values of the review data;
harvesting, at the at least one server and in an order based on the prioritizing, the item data and the review data from the data sources;
generating, at the at least one server and based on the harvesting, a data network having nodes corresponding in part to the plurality of items and the plurality of reviewers.
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Abstract
In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.
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Citations
15 Claims
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1. A method comprising:
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scanning, at at least one server, a network of data sources, thereby identifying item data for a plurality of items and review data from a plurality of reviewers for a plurality of the items; prioritizing, at the at least one server, the data sources based in part on the strength of correlations between the data sources and predictive values of the review data; harvesting, at the at least one server and in an order based on the prioritizing, the item data and the review data from the data sources; generating, at the at least one server and based on the harvesting, a data network having nodes corresponding in part to the plurality of items and the plurality of reviewers. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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Specification