Word Embedding Item Vector Representations for Recommendation Accuracy
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Solution Overview
Problem
Conventional recommendation models fail to accurately distinguish similarities between items, leading to inaccurate recommendations, as they rely on popularity rather than item relationships.
Innovation Solution
The use of word embedding models by computing devices to learn vector-space representations of items, organizing interactions into sessions, and building an item similarity matrix based on these representations to provide accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional co-occurrence models are used to generate recommendations based on user interaction data, then recommendations can be provided using simple item relationship counting, but the recommendations are inaccurate because the models cannot distinguish why interactions occurred between items
Solution Approach 1:
The patent transforms item similarity measurement from discrete co-occurrence counting to continuous vector space representation. Items are represented as vectors where the direction and magnitude encode semantic relationships, allowing nuanced similarity measurement through vector operations rather than simple frequency counting.
Solution Approach 2:
The patent replaces the mechanical counting system of co-occurrence models with a mathematical vector space system. Instead of mechanically counting how often items appear together, the system uses vector embeddings and geometric relationships in multidimensional space to represent and compute item similarities.
2Productivity
If popularity-based recommendations are provided, then frequently interacted items are easily identified and recommended, but the recommendations fail to capture actual item similarities and user intent
Solution Approach 1:
The patent changes the fundamental parameter for recommendation from item popularity (scalar frequency count) to item semantic similarity (vector-based relationship metric). This allows the system to recommend items based on their conceptual relationship to viewed items rather than their overall popularity in the system.
3Measurement precision
If word embedding models are used to learn vector-space representations of items, then accurate item similarity can be determined, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing item vector embeddings offline using training data, storing these representations for efficient retrieval. During recommendation generation, the system only needs to perform simple vector similarity computations rather than complex model training, significantly reducing online computational complexity.
Data Source
AI summary
Learning vector-space representations of items for recommendations using word embedding models is described. In one or more embodiments, a word embedding model is used to produce item vector representations of items based on considering items interacted with as words and items interacted with during sessions as sentences. The item vectors are used to produce item recommendations similar to currently or recently viewed items.