Locality Sensitive Hashing Index for Mobile Recommendation Accuracy
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Solution Overview
Problem
Recommender systems for mobile devices that use geographic location as implicit input often fail to reflect the user's current interests, while obtaining explicit user input is inconvenient and time-consuming.
Innovation Solution
A processor-executed method using locality sensitive hashing (LSH) to create an index of buckets with word or phrase embeddings derived from documents associated with geographic locations, allowing for the creation of a query embedding and mapping it to nearest neighbors based on similarity measures, which are then displayed to the user for selection, thereby providing location-based recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If geographic location is used as implicit input for recommender systems, then the system can automatically provide recommendations without user input, but the recommendations may not reflect the user's current interests
Solution Approach 1:
The patent introduces an intermediary component (the recommendation system that processes both location data and user feedback) that mediates between the implicit location input and the user's actual interests. This intermediary analyzes patterns in user behavior and feedback to bridge the gap between geographic location and user intent, allowing the system to infer interests without direct user input while improving accuracy over time.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with recommendations (such as selections, views, or explicit feedback) are continuously processed to refine future recommendations. This feedback loop allows the system to learn from user behavior patterns and improve the accuracy of interest prediction over time, resolving the contradiction between automatic operation and precision.
2Measurement precision
If explicit user input is obtained to determine user interests, then recommendation accuracy improves, but user convenience decreases due to time spent on data entry
Solution Approach 1:
The system applies self-service by automatically collecting and processing user feedback data without requiring explicit user input. The recommendation system monitors user interactions, analyzes behavior patterns, and generates recommendations autonomously, allowing users to benefit from accurate recommendations without investing time in data entry or explicit preference specification.
Solution Approach 2:
The system utilizes implicit feedback from user interactions (such as which recommendations are viewed or selected) to continuously improve recommendation accuracy. This feedback mechanism enables the system to learn user interests passively over time, achieving high precision without requiring users to spend time providing explicit input.
3Measurement precision
If a comprehensive index of all entities is maintained for accurate nearest neighbor search, then recommendation accuracy improves, but system complexity and memory requirements increase
Solution Approach 1:
The patent segments the comprehensive entity index into multiple localized indexes organized by geographic regions or clusters. Instead of maintaining a single global index of all entities, the system divides the index into manageable segments that can be stored and processed more efficiently in memory. This segmentation reduces overall system complexity while maintaining recommendation accuracy by performing nearest neighbor searches within relevant local segments.
Solution Approach 2:
The system applies local quality by optimizing the index structure and data storage specifically for memory-based operations. Rather than using complex disk-based indexing structures, the patent implements simplified index representations tailored for in-memory processing, reducing device complexity while preserving the accuracy needed for effective nearest neighbor search in the context of mobile device constraints.
4Speed
If an in-memory database is used for storing the index and cache, then search speed improves, but memory consumption increases
Solution Approach 1:
The patent extracts only the essential and frequently accessed data elements into the in-memory index and cache, rather than storing complete entity information. By selecting and storing only the most relevant features and metadata needed for nearest neighbor search, the system achieves fast in-memory processing while minimizing memory consumption, allowing the mobile device to maintain a comprehensive enough index without excessive memory usage.
Data Source
AI summary
Software for a website hosting short-text services creates an index of buckets for locality sensitive hashing (LSH). The software stores the index in an in-memory database of key-value pairs. The software creates, on a mobile device, a cache backed by the in-memory database. The software then uses a short text to create a query embedding. The software map the query embedding to corresponding buckets in the index and determines which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure. The software displays location types associated with each of the buckets that are nearest neighbors in a view in a graphical user interface (GUI) on the mobile device and receives a user selection as to one of the location types. Then the software displays the entities for the selected location type in a GUI view on the mobile device.