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Hashing-based effective user modeling

A user and hash code technology, applied in the field of user activity modeling and similarity search, can solve the problem of high computing cost

Pending Publication Date: 2021-10-22
SAMSUNG ELECTRONICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such a task is computationally expensive due to the large-scale nature of such data (which may involve tens of millions of users with constantly updated interaction histories, each spanning millions of sessions over time).

Method used

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  • Hashing-based effective user modeling
  • Hashing-based effective user modeling
  • Hashing-based effective user modeling

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Embodiment Construction

[0027] Hashing learning is widely adopted as a solution to approximate nearest neighbor search for large-scale data retrieval in a variety of applications. Applying deep architectures to learn hashing has particular benefits due to its computational efficiency and retrieval quality. However, these deep architectures may not be well-suited to correctly handle data known as "sequential behavior data". Sequential behavioral data may include data types observed in application scenarios related to user modeling. In certain embodiments, to learn a binary hash for sequential behavioral data, the system can capture users' evolving preferences (e.g., measured over extended time periods) or exploit user activity patterns on different time scales (e.g., , by comparing activity patterns on short and long timescales). The disclosed techniques provide novel deep learning-based architectures to learn binary hashes for sequential behavioral data. The effectiveness of the architecture of th...

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Abstract

In one embodiment, a method includes receiving user behavior data and contextual information associated with the user behavior data, the contextual information including a first data portion associated with a first context type. The method includes generating, from the user behavior data and the contextual information using a hashing algorithm, a first heterogeneous hash code including a first portion representing the user behavior data and a second hash code portion representing the first data portion associated with the first context type. The method includes accessing a second heterogeneous hash code including a third hash code portion representing a second data portion associated with the first context type. The method includes comparing the first heterogeneous hash code with the second heterogeneous hash code including determining similarity between the second hash code portion of the first heterogeneous hash code and the third hash code portion of the second heterogenous hash code.

Description

technical field [0001] The present disclosure generally relates to user activity modeling and similarity searching. Background technique [0002] In big data systems for advertising and marketing, finding and ranking similar groups of users (this is called nearest neighbor search) is a key task, especially for tasks such as lookalike search, user segmentation, etc. (user segmentation) and recommendation applications. Many types of modern devices, including TVs and mobile devices, have detailed profiles of users' interaction history with content, such as live TV, video-on-demand, games, apps, and external devices, which can be used to calculate the interaction between users. similarity, and finally calculate their "nearest neighbors". However, such a task is computationally expensive due to the large-scale nature of such data, which may involve tens of millions of users with constantly updated interaction histories, each spanning millions of sessions over time. [0003] On...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2455G06Q30/02
CPCG06Q30/0201G06Q30/0276G06Q30/0205G06N3/084G06N3/045G06F16/2455G06F16/24575G06Q30/0255G06N3/08G06F16/2264G06N20/00G06F17/15
Inventor 周鹏朱英南赵湘源金洪会H.李
Owner SAMSUNG ELECTRONICS CO LTD
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