Abstract: A user inputs a request (1) for a commodity recommendation. A computer system accesses (2) a plurality of commodity reviews. The computer system extracts feature indicators (3) and sentiment indicators (4) from each commodity review. The computer system determines (5) the popularity of each feature indicator and the similarity between a first commodity (Q) and a second commodity (C). The computer system evaluates the sentiment indicators and evaluates the similarity indicator to form (7) the commodity recommendation. After the commodity recommendation has been formed in step (7), the computer system delivers (8) the commodity recommendation for the second commodity (C) to the user using a website interface.

Abstract: A method for generating product recommendations comprises analyzing a database of messages, comprising a set of messages posted by users of a micro-blogging service to generate a user index and a product index. The user index comprises for each of a plurality of users of the system, a ranked set of terms included by the user in their posted messages. The product index comprises for each product which is to be potentially recommended, a ranked set of terms derived from messages posted by users and referencing the product. Responsive to a query identifying a user, the user index for the user is compared to the product indices to return a limited set of product identifiers corresponding to product indices most similar to the user index. The set of product identifiers are provided as recommendations to a service provider.