Recommendation method and server

Abstract

Disclosed are a recommendation method and server. In the method, recommendation lists sent by various recommendation systems are combined by using a preset combining policy, to obtain multiple recommendation models, or a system feeds back a recommendation result to a user, the combined recommendation list is updated according to a selection probability fed back by the user, to implement real-time estimation, a selection result periodically fed back by the user is received, and the combined recommendation list is updated, to reflect both a current interest and a historical interest of the user.

Description

technical field [0001] The invention belongs to the field of data processing, in particular to a recommended method and server. Background technique [0002] In today's information explosion, more and more commercial systems introduce recommendation technology, changing from the previous mode of people looking for content to content looking for people to meet the personalized needs of users. The effect of a single recommendation system recommendation result is limited, which is especially reflected in more and more recommendation competitions. The final winners of the competition often use multiple recommendation technologies / models or scoring results for fusion and integration. In the past, the recommendation was more analogous to the problem of predictive scoring, but some scholars believe that the form of recommendation list may be more suitable. The effect of the previous recommendation system is not easy to evaluate, and it is usually done offline, and the real-time eff...

AI Technical Summary

Problems solved by technology

The disadvantage of existing technology 2 is that the recommendation technology is not easy to expand, and each time a recommendation technology is added, the combination model needs to be re-trained offline, and it is impossible to feed back the user's current interest in real time

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