LLM-Mediated Book Search for Intent-Based Recommendations
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Solution Overview
Problem
Existing systems struggle to accurately determine the real needs of users during book search, leading to reduced accuracy in book recommendations and increased search costs, especially for long content, which can deter users from reading.
Innovation Solution
A method and apparatus that acquires user query information, determines book search intent using a generative language model, identifies matching books, and provides personalized recommendations based on intent, including various content carriers and reading progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional book search systems are used, then users can perform basic book searches, but the system cannot accurately determine real user needs, leading to reduced recommendation accuracy
Solution Approach 1:
The patent introduces a large language model as an intermediary between the user's query and the book database. The LLM acts as a mediator that translates user queries into structured search conditions and generates natural language recommendations, thereby improving the system's ability to understand user intent and provide accurate recommendations without losing information in the process.
Solution Approach 2:
The patent changes the parameter of information processing by using a large language model to transform the way user queries are handled. Instead of traditional keyword matching, the system uses the LLM to understand the semantic meaning of queries, extract search conditions, and generate recommendations based on multiple dimensions including user preferences and book features, thereby improving recommendation accuracy.
2Productivity
If comprehensive book search is performed, then more books can be found, but search costs increase and users may be deterred from reading long content
Solution Approach 1:
The patent applies preliminary action by pre-processing user queries through the large language model to extract search conditions and generate structured search requests before actually searching the book database. This preliminary step organizes the search parameters and filters potential results, making the subsequent search process more efficient and reducing the time users need to spend searching for books.
3Adaptability or versatility
If personalized recommendations are provided, then user satisfaction improves, but the system complexity increases
Solution Approach 1:
The patent applies universality by using a single large language model to perform multiple functions: understanding user queries, extracting search conditions, generating search requests, and formulating recommendations. This multi-functional approach enables personalized recommendations without requiring separate complex systems for each function, thereby maintaining relatively simple system architecture while achieving high adaptability.
Data Source
AI summary
According to embodiments of the disclosure, a method, an apparatus, a device and a medium for book search are provided. The method includes: acquiring query information of a user; determining a book search intent of the user at least based on the query information; determining at least one book matching the book search intent; determining recommendation information for recommending the at least one book to the user at least based on the book search intent; and providing a response to the query information to the user, the response including an indication of the at least one book and the recommendation information. Thereby, it is possible to quickly and accurately meet a user's personalized book search needs, and providing the recommendation information that better matches the intent can improve user satisfaction and conversion rate of book recommendation.