Personalized POI Sorting via User Preference Scores
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Solution Overview
Problem
Existing map applications fail to provide personalized sorting of hotel Point Of Interest (POI) results based on user preferences, leading to unsatisfactory user experiences as they treat all users equally regardless of individual preferences.
Innovation Solution
A method that integrates user preference scores into the sorting model by using a preference model to assign preference scores to candidate geographic location points, which are then used as sorting features to determine personalized query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If candidate hotel POIs are sorted based on hotel features such as geographic location, evaluation, star rating, price and popularity, then the sorting results are consistent and reliable, but the sorting cannot meet users' personalized needs
Solution Approach 1:
The sorting system is divided into two independent modules: a preference model that captures user-specific characteristics and a sorting model that performs the actual ranking. The preference model is segmented into separate feature extraction and scoring components, allowing each to be optimized independently while maintaining overall system functionality.
Solution Approach 2:
A preference model is introduced as an intermediary component between the user and the sorting model. This preference model processes user-specific features and generates preference scores that are then fed into the sorting model, acting as a mediator that translates user characteristics into actionable sorting signals without requiring the sorting model itself to be overly complex.
2Loss of information
If user preference scores are integrated into the sorting model, then personalized query results can be provided, but the computational complexity increases
Solution Approach 1:
User preference scores are pre-computed by the preference model before the actual sorting operation. This preliminary action captures user-specific characteristics in advance, so that during query processing, only the integration of pre-computed scores with POI features is needed, significantly reducing real-time computational energy requirements.
Solution Approach 2:
The preference model serves itself by automatically extracting and scoring user-specific features without requiring manual intervention or complex configuration. The model self-adapts to user preferences through automated feature processing, reducing the energy overhead associated with manual preference configuration and system reconfiguration.
3Productivity
If the same geographic location sorting results are provided for all users, then the sorting process is simple and fast, but it fails to meet individual user preferences
Solution Approach 1:
The sorting system applies local quality by tailoring the sorting results to each user's specific preferences while maintaining the same underlying sorting framework. The preference model captures user-specific local characteristics, and these are integrated into the sorting process to produce personalized results without fundamentally changing the overall sorting mechanism, thus maintaining speed while improving satisfaction.
Data Source
AI summary
A method for sorting geographic location points, a method for training a sorting model and corresponding apparatuses are disclosed, which relates to the technical field of big data. A specific implementation solution is: receiving a query request for geographic location points of a vertical class from a user; inputting candidate geographic location point data of the vertical class into a preference model of the user, to obtain a preference score of the user for each candidate geographic location point; inputting the preference score of the user for each candidate geographic location point into a sorting model as one of sorting features of each candidate geographic location point, to obtain a sorting score of each candidate geographic location point; and determining, according to the sorting score of each candidate geographic location point, a query result returned to the user. The present disclosure can integrate preference factors of a user into sorting when the user queries geographic location points of a vertical class, so that query results can meet the user's personalized needs.