Food and beverage recommendation algorithm based on rating and feature similarity in social network

Abstract

The invention discloses a food and beverage recommendation algorithm based on rating and feature similarity in a social network. The method mainly comprises the following steps: (1) building a user rating library in the current social network; (2) calculating the rating similarity between Ua and other users in the user rating library; (3) selecting k users having highest rating similarity with the Ua to obtain a rating similarity recommendation user set; (4) constructing a feature similarity model M=([chi]a, i, [delta]a, i, [sigma]a, i, [gamma]a, i) of the Ua; and (5) calculating feature similarity recommendation levels, and recommending top ranked items to a target user. According to the food and beverage recommendation algorithm, the feature similarity among users is taken as a second basis for the recommendation algorithm other than conventional rating similarity, and various user features are calculated, so that the attack cost is increased greatly; the attack resistance is enhanced greatly; and the recommendation accuracy is increased.

Description

technical field [0001] The invention relates to the field of recommendation algorithms in social networks, in particular to a food and beverage recommendation algorithm based on score and feature similarity and applied to social networks. Background technique [0002] As a hot issue, personalized service has been concerned and studied by many research fields. An important research content of personalized service is personalized recommendation, which can find user groups with similar interests according to user interests, and then recommend information of interest to each other among user groups. Use personalized recommendation technology to recommend restaurants suitable for users, so that users can quickly obtain food and restaurant information that conforms to their consumption habits. For the system, it can not only gain the trust of users, but also gain more favor and use. , It also makes the content recommended by the system more accurate and can better serve users. ...

AI Technical Summary

Problems solved by technology

[0005] In view of the shortcomings of traditional recommendation algorithms, in order to solve the problems of low recommendation accuracy and system vulnerability, and at the same time when big data is widely used in e-commerce, by comprehensively examining the connections of social networks and with the help of a large amount of effective data aggregation, To achieve a more accurate catering recommendation service, the present invention proposes a catering recommendation algorithm based on similar scores and features in social networks

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