Content recommendation based on efficacy models

Abstract

The disclosure includes a system and method for providing recommendation items to users. The system includes a processor and a memory storing instructions that when executed cause the system to: receive identification input data associated with a user; identify the user using the identification input data; receive sensor data; determine a current situation associated with the user from one or more predefined situations described by predefined situation data and associated parameters; receive data describing a set of candidate items; generate a set of efficacy scores for the set of candidate items; eliminate one or more candidate items from the set of candidate items to obtain one or more remaining candidate items; rank the one or more remaining candidate items based on one or more associated efficacy scores; and provide one or more recommendation items to the user from the one or more ranked remaining candidate items.

Description

technical field [0001] This instruction manual refers to content recommendation. In particular, this specification relates to a system for providing recommended items to users according to an efficacy model. Background technique [0002] While driving a vehicle, a user may request content items from the content recommendation system. For example, a user may request that a podcast be played in an in-vehicle infotainment system. However, existing content recommendation systems often recommend content items to users based on what they want, but cannot recommend content items based on what users need in a specific context. For example, existing content recommendation systems may provide audio items to users in situations where the recommendation of audio items is ineffective and distracts the user from driving. Contents of the invention [0003] According to one innovative aspect of the subject matter described in this disclosure, a system for providing recommended items to...

AI Technical Summary

Problems solved by technology

However, existing content recommendation systems often recommend content items to users based on what the user wants, but cannot recommend content items based on what the user needs in a specific context.

For example, existing content recommendation systems may provide audio items to users in situations where the recommendation of audio items is ineffective and distracts the user from driving.

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