Cannabis Recommendation System Using Video Feedback
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Solution Overview
Problem
Existing methods for recommending cannabis strains are ineffective due to each individual's unique endocannabinoid system, as they rely on general user similarities or content-based suggestions rather than personalized factors.
Innovation Solution
A machine learning-based recommendation system that collects user data on lifestyle, preferences, and medical conditions, clusters this data with other users, and creates personalized cannabis product packages based on user profiles, with feedback loops to refine strain recommendations using pre- and post-consumption video analysis and THC effectiveness scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods for recommending cannabis strains are used (based on user similarities or content-based suggestions), then the recommendation process is simple and easy to implement, but the effectiveness is low due to not accounting for individual endocannabinoid system differences
Solution Approach 1:
The system segments users into distinct clusters based on their endocannabinoid system characteristics, lifestyle factors, and consumption patterns. This segmentation allows for personalized recommendations tailored to each user's unique biological profile rather than using generic similarity-based approaches.
Solution Approach 2:
The system changes the parameters used for recommendation from simple user similarity metrics to comprehensive profiles including endocannabinoid system state, lifestyle factors, consumption history, and genetic markers. This parameter transformation enables more accurate and personalized strain recommendations.
2Measurement precision
If personalized user profiles with detailed data collection are implemented, then the recommendation accuracy improves, but the data collection complexity and processing requirements increase
Solution Approach 1:
The system implements feedback loops where consumption results and user responses are continuously collected and used to refine and update user profiles. This feedback mechanism improves measurement precision over time while the automated nature of the feedback collection manages the complexity of data processing.
Solution Approach 2:
Users actively participate in profile creation and updates by providing lifestyle information, consumption feedback, and preference data. This self-service approach reduces the burden on the system for data collection while maintaining high profile accuracy through user-provided information.
3Adaptability or versatility
If machine learning models are used to analyze user data and recommend strains, then the personalization level increases, but the computational resources and time required for analysis increase
Solution Approach 1:
The system performs preliminary clustering and profile creation during off-peak times or in batch processing mode, preparing user profiles and strain recommendations in advance. This preliminary action reduces real-time analysis requirements when users actually need recommendations.
Solution Approach 2:
The system applies different levels of analysis complexity to different users based on their needs and data availability. Not all users require the full computational intensity of complete machine learning analysis, allowing for optimized resource allocation and reduced average analysis time.
Data Source
AI summary
User data is collected regarding the user's lifestyle, preferences, and medical conditions and used to recommend a cannabis product. The collected user data is clustered into a group with user data from other users to create a user profile, and a package of cannabis products is created and provided to the user based on the user profile. A recommendation engine recommends a strain of cannabis product within the package to the user based on a purpose or occasion provided by the user for the cannabis consumption. To evaluate the effectiveness of the recommendation for the given purpose or occasion, pre-consumption and post-consumption video data of the user is captured and processed by a deep learning system for the selected strain. Feedback data is also collected from the user to feed back to fine tune the clustering and the contents of the package recommended for the user profile.