Recommendation Engine Using Media Usage Data
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Solution Overview
Problem
Existing systems face challenges in generating accurate and relevant purchasing recommendations for online merchants, leading to suboptimal sales and customer engagement.
Innovation Solution
An electronic commerce network with a recommendations engine that utilizes interaction history and media usage data from various devices to provide personalized product suggestions, weighing recent interactions and media consumption patterns to enhance recommendation relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems are used, then implementation is simple, but recommendation accuracy and relevance are low
Solution Approach 1:
The patent segments the recommendation system into multiple independent components: interaction history analysis module, media usage data processing module, weighting module, and recommendation generation module. Each component handles a specific aspect of the recommendation process, allowing for improved accuracy through specialized processing while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of analysis by incorporating media usage data from portable media players alongside traditional e-commerce interaction history. This multi-dimensional approach combines behavioral data from different contexts (purchasing patterns and media consumption patterns) to create more accurate and relevant recommendations that consider the customer's broader interests and preferences.
2Measurement precision
If comprehensive customer data is analyzed, then recommendation relevance improves, but data processing time increases
Solution Approach 1:
The system performs preliminary analysis and weighting of interaction history and media usage data in advance, before generating specific recommendations. By pre-processing and organizing customer data, assigning weights to different data sources, and preparing aggregated profiles beforehand, the system reduces the computational burden during real-time recommendation generation, thus maintaining high relevance while minimizing processing time delays.
3Reliability
If multiple data sources are integrated, then recommendation quality improves, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components that facilitate integration between different data sources: an interaction history analysis module that processes e-commerce data, a media usage data processing module that handles portable media player data, and a weighting module that harmonizes these diverse data sources. These intermediaries translate and standardize data from different formats and contexts into a unified recommendation framework, improving quality while managing integration complexity through specialized interface layers.
Data Source
AI summary
Disclosed are various embodiments of systems, methods, and programs embodied in computer readable mediums for generating item purchase recommendations. To provide such a recommendation, first data is accessed using a server, the first data comprising an interaction history of an entity with respect to at least one network site. Also, second data is accessed using the server, the second data comprising a record of use of media by the entity on at least one media device remote to the server. An item recommendation is generated in the server for the entity based on the first and second data.