Personalized Recommendation via Latent Topic Space Mapping
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Solution Overview
Problem
Traditional recommendation systems are unsuitable for online marketplaces due to their volatile inventory and unstructured data, making it difficult to obtain reliable relations between items and model user preferences effectively.
Innovation Solution
Mapping users and items to a common latent topic space using search queries allows for novel collaborative filtering, enabling stable relations and personalized recommendations, employing Latent Dirichlet Allocation and matrix factorization models to analyze search queries and build user and item profiles independently of individual user-item relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional recommendation systems are used in online marketplaces, then user preferences can be modeled, but the volatile inventory and unstructured data make it difficult to obtain reliable relations between items
Solution Approach 1:
The patent segments the recommendation problem into two independent components: user modeling based on search queries and item profiling based on item descriptions. This segmentation allows each component to be optimized independently, with user preferences captured through query analysis and item characteristics through textual profiling, thereby achieving reliable item relations despite inventory volatility.
Solution Approach 2:
The patent introduces search queries as an intermediary element that bridges users and items. Instead of directly modeling user-item relationships which are sparse and unreliable in volatile environments, the system uses search queries as a mediator to capture user intent and map it to relevant items, enabling reliable recommendations without requiring stable item inventories.
2Measurement precision
If user profiles and past behavior are used for personalization, then relevant information can be provided, but the system cannot capture user intentions in volatile environments
Solution Approach 1:
The patent performs preliminary action by analyzing and storing user search queries before actual item selection occurs. By capturing user intent through search query analysis in advance, the system builds user profiles that reflect current interests rather than relying solely on historical purchase behavior, enabling more precise and adaptive personalization in volatile environments.
Solution Approach 2:
The patent substitutes traditional mechanical recommendation systems that rely on explicit user profiles and purchase history with a query-based approach. Instead of using conventional collaborative filtering that requires dense user-item interaction data, the system replaces it with text analysis of search queries and item descriptions, enabling intention capture without requiring stable mechanical interaction patterns.
3Productivity
If collaborative filtering is applied, then personalized recommendations can be generated, but the volatile inventory makes obtaining stable user-item relationships difficult
Solution Approach 1:
The patent segments the collaborative filtering process into independent user modeling and item profiling components. User models are built from search query patterns while item profiles are constructed from textual descriptions, allowing recommendations to be generated by matching these independent components without requiring stable user-item relationship data, thus maintaining productivity in volatile inventory conditions.
Solution Approach 2:
The patent uses search queries as an intermediary to enable collaborative filtering without direct user-item relationship stability. By analyzing query patterns that connect users to items through intermediate terms and concepts, the system can generate personalized recommendations even when the direct user-item relationships are unstable or sparse due to inventory volatility.
Data Source
AI summary
A system, method, and machine readable medium for creating a personalized recommendation of an item by creating a topic vector based on a plurality of search queries, at least one of a plurality of users associated with the search queries or a plurality of items associated with the search queries; interring a topical preference for a user based on a search query by the user; and recommending at least one item based on the topical preference and the topic vector.