Recommender System Using Trendsetter Inference for Early Item Prediction
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Solution Overview
Problem
Conventional recommendation systems are limited in providing valuable recommendations as they rely solely on items consumed by users or general user behavior patterns, failing to suggest items that are ahead of the curve, leading to user dissatisfaction and inefficiencies in recommendation generation.
Innovation Solution
A recommender system based on trendsetter inference generates a trend setting score for users, identifying individuals who are ahead of the curve in consuming items that will become popular, and recommends items consumed by these trendsetters with whom the user has a high affinity, even if those items are not currently popular.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional collaborative filtering or content-based recommendation systems are used, then the system can generate recommendations based on user behavior patterns or previously consumed items, but the recommendations are limited to items already consumed by users or similar users rather than being ahead of the curve
Solution Approach 1:
The patent applies preliminary action by identifying and acting on early adopters who consume items before they become popular. The system proactively detects users who consume items in advance of the general population and uses their behavior patterns to generate recommendations for other users before those items become widely known. This allows the system to recommend items ahead of the curve rather than only after they have been widely consumed.
Solution Approach 2:
The patent introduces early adopters as intermediaries between item creators and the general user population. These early adopters serve as a bridge who consume and validate items before they become popular, and their consumption patterns are used as a mediating signal to generate recommendations for other users. This intermediary layer enables the system to identify promising items before they achieve widespread popularity.
2Ease of manufacture
If recommendations are based only on items consumed by the user or general mass of users with similar behavior patterns, then the system can generate recommendations using available data, but the recommendations lack value because they are not ahead of the curve
Solution Approach 1:
The system performs preliminary action by proactively identifying early adopters and their consumption patterns before items become popular. Instead of waiting for widespread adoption to detect trends, the system anticipates trends by monitoring users who consume items in advance. This allows the system to capture trend information earlier and generate recommendations before the general population adopts the items.
Solution Approach 2:
The patent replaces the conventional mechanical approach of analyzing only user similarity or item similarity with a new mechanism that incorporates early adopter detection. Instead of relying solely on collaborative filtering algorithms that compare user behavior patterns, the system introduces a new mechanism for identifying users who exhibit early adoption behavior and uses their consumption patterns as a leading indicator of emerging trends.
Data Source
AI summary
A trend setting score that identifies a degree of trend setting exhibited by a user is generated for each of multiple users. This degree of trend setting exhibited by the user is an indication of how well the user identifies trends for items (e.g., consumes items) prior to the items becoming popular. The item consumption of users with high trend setting scores is then used to identify items that are expected to become popular after a lag in time. For a given user, another user with a high trend setting score (also referred to as a trendsetter) and having a high affinity with (e.g., similar item consumption behavior or characteristics) the given user is identified. Recommendations are provided to the given user based on items consumed by the trendsetter prior to the items becoming popular.