Query-Aware Sequential Search for Digital Content
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Solution Overview
Problem
Conventional sequence-based search techniques face challenges due to data sparsity and noisy, complex relationships between items, leading to inaccuracies and inefficient use of computational resources when attempting to infer search intent for non-textual digital content.
Innovation Solution
The implementation of a query-aware sequential search system that leverages search queries as contextual signals to improve accuracy by incorporating query information into sequences, expanding training data, and using graph-based sequence augmentation to enhance machine-learning model generalization and training accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text-based techniques are used to match search intent with digital content tags, then the search process can be implemented, but accuracy deteriorates due to errors in matching text tags with non-textual content intent
Solution Approach 1:
The patent introduces search queries as an intermediary element that mediates between the user's intent and the digital content tags. Instead of directly matching user intent with content tags, the system uses the actual search queries users submit as intermediate representations of intent, which are then used to train the machine learning model. This intermediary layer resolves the mismatch between textual tags and non-textual content understanding.
Solution Approach 2:
The patent replaces the conventional mechanical text-matching system with a machine learning-based semantic understanding system. Instead of using rule-based or keyword-matching approaches to compare search queries with content tags, the system employs neural networks to learn semantic relationships from training data, enabling more accurate understanding of user intent for non-textual content.
2Adaptability or versatility
If conventional sequence-based techniques leverage sequences of interaction with past digital content to identify search intent, then contextual information is utilized, but performance deteriorates due to data sparsity and noisy complex relationships between items
Solution Approach 1:
The patent applies preliminary action by collecting and storing search query sequences in advance before the actual search task. The system accumulates user search behavior data over time, building a comprehensive training dataset that captures the temporal relationships and patterns in user interactions. This pre-collected data is then used to train the machine learning model, enabling it to make accurate predictions even when individual user sequences are sparse or noisy.
Solution Approach 2:
The patent changes the parameters of the sequence modeling by incorporating search queries as explicit temporal indicators in the sequence data. Instead of treating sequences as simple ordered lists of content interactions, the system enriches the sequence representation with query text and temporal information, transforming the input parameters to better capture the nuanced relationships in user behavior patterns.
3Measurement precision
If repeated searches are performed to overcome data sparsity and improve accuracy, then search quality improves, but computational resource usage increases
Solution Approach 1:
The patent performs preliminary action by pre-training a machine learning model offline using large amounts of historical search data. This pre-training phase, which would otherwise require extensive computational resources if performed repeatedly during actual searches, is conducted once in advance. The trained model is then deployed for making predictions during actual search operations, significantly reducing the computational resources needed during online query processing while maintaining high accuracy.
Data Source
AI summary
Digital content search techniques are described that overcome the challenges found in conventional sequence-based techniques through use of a query-aware sequential search. In one example, a search query is received and sequence input data is obtained based on the search query. The sequence input data describes a sequence of digital content and respective search queries. Embedding data is generated based on the sequence input data using an embedding module of a machine-learning model. The embedding module includes a query-aware embedding layer that generates embeddings of the sequence of digital content and respective search queries. A search result is generated referencing at least one item of digital content by processing the embedding data using at least one layer of the machine-learning model.