Weighting User Feedback by Device Context
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Solution Overview
Problem
Existing music recommendation systems fail to accurately measure user intensity in ratings, leading to suboptimal song selections, as they do not consider the effort or context in which ratings are provided.
Innovation Solution
The system determines the intensity of user ratings by analyzing contextual information such as device activity, location, and user behavior, using this data to adjust the likelihood of song recommendations and improve playlist generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user ratings are processed without contextual analysis, then the recommendation system is simple and fast, but the accuracy of recommendations deteriorates
Solution Approach 1:
The system pre-collects contextual information (device activity, location, user behavior patterns) before rating events occur, storing this data in advance so that when a rating is provided, the intensity can be immediately determined without real-time processing delays. This preliminary data gathering resolves the contradiction by preparing measurement data ahead of time.
Solution Approach 2:
The patent introduces contextual information as an intermediary element that mediates between the simple rating input and the complex recommendation output. Instead of directly processing ratings to generate recommendations, the system uses contextual data (device state, location, behavior patterns) as a bridge to infer user intensity, thereby improving accuracy without requiring the recommendation engine itself to become overly complex.
2Measurement precision
If contextual information is collected and analyzed, then the accuracy of recommendation improves, but the processing time and computational resources increase
Solution Approach 1:
Contextual information such as device activity states, location data, and user behavior patterns are collected and stored in advance before rating events occur. This pre-collection eliminates the need for real-time data gathering during the rating process, significantly reducing processing time while maintaining high recommendation accuracy.
Solution Approach 2:
The system automatically collects and processes contextual information without requiring additional user input or interaction. The device's own operational data (screen state, application usage, location) serves itself as the contextual source, eliminating time-consuming manual data collection while improving recommendation accuracy through automated analysis.
3Adaptability or versatility
If multiple contextual parameters are considered, then the personalization of recommendations improves, but the data processing complexity increases
Solution Approach 1:
The patent segments contextual information into distinct categories (device activity state, location data, user behavior patterns) and processes each segment independently. By dividing the complex contextual data into manageable segments, the system can analyze multiple parameters without overwhelming processing complexity, thereby achieving high personalization through modular data handling.
Solution Approach 2:
The system applies different levels of analysis to different contextual parameters based on their relevance to specific recommendation scenarios. Not all contextual data is processed with equal depth - the system selectively intensifies analysis of locally relevant parameters (such as device activity during music playback) while using other parameters more simply, thereby achieving personalized recommendations without uniform high complexity across all data.
Data Source
AI summary
The present disclosure provides computer-implemented systems and processes for augmenting user ratings of items, such as a rating of a song playing on a user device, by analyzing contextual information, such as user and/or device activity data associated with the device. The contextual information may be used to determine an associated intensity of the rating or feedback. The determined intensity levels can be used to weigh the associated rating events and improve the quality of item recommendations that are based on such ratings. Contextual information may indicate whether the user transitioned from another application to rate an item, device status information, and so on. In one embodiment, contextual information can be used to assess how intensely the user feels about a music item and to provide improved music recommendations, such as songs to provide in a playlist or radio station, based on the intensity.