Big data based cross-domain recommendation method and apparatus
First Claim
1. A big data based cross-domain recommendation method, comprising:
- modeling a topic separately based on an online input record and an offline behavior record of a user in a specific user set, the user in the specific user set having both the online input record and the offline behavior record;
determining a transition probability of transitioning from each online input topic to each offline behavior topic according to a topic modeling result; and
recommending content of an offline behavior to a target user based on the transition probability and an online input record of the target user.
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Abstract
The present disclosure discloses a big data based cross-domain recommendation method and apparatus. Correlation between domains and correlation between user behavior characteristics are obtained by connection and cross analysis of a user'"'"'s online input and offline behaviors in different domains, and content is recommended to the user according to the established correlation. The technical solution of the present disclosure is applied to the precise consumer brand recommendation to users and the precise positioning of potential consumer brand customers in the Internet+retail area. The technical solution can solve a series of problems, such as cross-drainage of users in multiple domains, precise marketing and precise positioning of potential customers, and the effect is very obvious. From the offline simulation test and online real consumption test, the brand recommendation and user positioning accuracy is largely improved, while the GMV of the offline retail is largely improved.
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Citations
13 Claims
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1. A big data based cross-domain recommendation method, comprising:
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modeling a topic separately based on an online input record and an offline behavior record of a user in a specific user set, the user in the specific user set having both the online input record and the offline behavior record; determining a transition probability of transitioning from each online input topic to each offline behavior topic according to a topic modeling result; and recommending content of an offline behavior to a target user based on the transition probability and an online input record of the target user. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A big data based cross-domain recommendation apparatus, comprising:
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at least one processor; and a memory storing instructions, which when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising; modeling a topic separately based on an online input record and an offline behavior record of a user in a specific user set, the user in the specific user set having both the online input record and the offline behavior record; determining a transition probability of transitioning from each online input topic to each offline behavior topic according to a topic modeling result; and recommending content of an offline behavior to a target user based on the transition probability and an online input record of the target user. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-volatile computer storage medium storing a computer-readable instruction capable of being executed by a processor, wherein when the computer-readable instruction is executed, the processor executes a big data based cross-domain recommendation method, the method comprising:
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modeling a topic separately based on an online input record and an offline behavior record of a user in a specific user set, the user in the specific user set having both the online input record and the offline behavior record; determining a transition probability of transitioning from each online input topic to each offline behavior topic according to a topic modeling result; and recommending content of an offline behavior to a target user based on the transition probability and an online input record of the target user.
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Specification