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US 2025/0053801 A1Published application
Multi-task learning for dependent multi-objective optimization for ranking digital content
AssigneeMicrosoft Technology Licensing, LLC
InventorsXiaojing Chen, Jiong Zhang, Sen Zhou, Zhenjie Zhang
Life of this patent
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Abstract
Embodiments of the disclosed technologies are capable of providing a ranking of digital content using a machine learning model. The machine learning model is configured for multi-task learning for dependent multi-objective optimization. Embodiments configure a memory according to a machine learning model, where the machine learning model includes a shared backbone and multiple heads. Each of the multiple heads are trained to perform a task associated with a first objective of a second objective.
Claims20
A method comprising:
- configuring a memory according to a machine learning model, wherein the machine learning model comprises a shared backbone and a plurality of heads each trained to perform a task associated with either a first objective or a second objective, wherein:
- an output of the shared backbone is input into:
- a first head of the plurality of heads, wherein the first head is trained to perform a first task associated with the first objective,
- a second head of the plurality of heads, wherein the second head is trained to perform a first task associated with the second objective,
- a third head of the plurality of heads, wherein the third head is trained to perform a second task associated with the first objective, and
- an output of the first head is input into the second head of the plurality of heads,
- an output of the second head is input into a fourth head of the plurality of heads,
- an output of the third head is input into the fourth head of the plurality of heads, and
- an output of the fourth head of the plurality of heads, wherein the output is based on the first objective and the second objective.
6 more elements
A system comprising:
- at least one processor;
- and
- at least one memory, wherein the at least one memory is configured according to a machine learning model comprising a shared backbone and a plurality of heads each trained to perform a task associated with either a first objective or a second objective, wherein the first objective contradicts the second objective, and wherein:
- an output of the shared backbone is input into:
- a first head of the plurality of heads, wherein the first head is trained to perform a first ranking task associated with the first objective,
- a second head of the plurality of heads, wherein the second head is trained to perform a first ranking task associated with the second objective,
- a third head of the plurality of heads, wherein the third head is trained to perform a second ranking task associated with the first objective, and
- an output of the first head is input into the second head of the plurality of heads,
- an output of the second head is input into a fourth head of the plurality of heads,
- an output of the third head is input into the fourth head of the plurality of heads, and
- an output of the fourth head of the plurality of heads ranks a search result according to the first objective and the second objective.
8 more elements
A non-transitory computer-readable medium storing executable instructions, which when executed by a processing device, cause the processing device to perform operations comprising:
- obtaining a search result including a plurality of entries associated with a search query;
- inputting the search result into a machine learning model trained to rank the search result according to a first objective and a second objective, wherein the machine learning model comprises:
- a shared backbone outputting a feature representation into:
- a first head of a plurality of heads, wherein the first head is trained to perform a first task associated with the first objective,
- a second head of the plurality of heads, wherein the second head is trained to perform a first task associated with the second objective,
- a third head of the plurality of heads, wherein the third head is trained to perform a second task associated with the first objective, and
- an output of the first head is input into the second head of the plurality of heads,
- an output of the second head is input into a fourth head of the plurality of heads,
- an output of the third head is input into the fourth head of the plurality of heads, and
- an output of the fourth head of the plurality of heads ranks the search result.
7 more elements
Dependencies are inferred from the claim text.