Individual gourmet recommending method based on position

Abstract

The invention discloses an individual gourmet recommending method based on the position. The distance similarity between a user and a merchant is calculated; the similarity between a user ui and a user uj is calculated; the predicted preference index of a target user to a restaurant is calculated according to the similarity between the user ui and the user uj; the similarity between an item i and an item j is calculated; the recommending preference index to a user u based on the item t is calculated; a recommending result is given according to the distance similarity between the user and the merchant, the predicted preference index of the target user to the restaurant, and the recommending preference index to the user u based on the item t. The individual gourmet recommending method has the advantages that the individual restaurant meeting the user requirement is provided for the user according to the current user position, the user grade for the merchant and the attribute of the user and the merchant, and the gourmet recommending efficiency and accuracy are improved. Meanwhile, the accurate recommending result can be converted into the consuming behavior, and the user satisfaction and merchant benefits are improved.

Description

technical field [0001] The invention belongs to the technical field of electronic commerce, and relates to a position-based personalized food recommendation method. Background technique [0002] Collaborative filtering algorithm is an algorithm based on the concept of "dividing people into groups", that is, people with the same interest preferences have similar preferences for commodities, that is, if a certain type of users have the same interest preferences, then the The user's ratings on the product objects will also be similar. Therefore, the most important thing for collaborative filtering recommendation is to find the nearest neighbors that are similar to the target user's interest preferences, and predict the target user's rating of the unrated recommended objects based on the ratings of the nearest neighbors on the recommended objects. Score, select several recommended objects with the highest predicted scores as the recommendation results to feed back to the user. A...

AI Technical Summary

Problems solved by technology

3. Intelligent and automated recommendation: Most of the collaborative filtering recommendation systems now require users to display and input scoring information to provide services. Although active participation of users in obtaining information can improve the accuracy of information, it is also necessary for users to use the system. Inconvenience is caused, and the degree of participation of users is also uneven, and the comprehensiveness of obtaining information is also greatly reduced

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