Inferring Recipe Temporal Attributes via Content Analysis
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Solution Overview
Problem
Existing recipes lack standardized formats and metadata for seasonal or holiday associations, making it difficult for users to find temporally relevant recipes, as existing metadata tags are incomplete and subjective descriptions are hard to interpret accurately for natural language processing.
Innovation Solution
A system that infers temporal attributes of recipes by generating attribute features from ingredients and preparation steps, using an attribute model to determine if a recipe is associated with a specific season or holiday, and stores these attributes for user-specific recommendations based on current time and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If metadata tags are used to categorize recipes by holiday or season, then recipe organization is improved, but such metadata tags are available only for a subset of recipes and do not include temporal categorization
Solution Approach 1:
The system automatically infers temporal attributes from recipe content without requiring manual metadata tagging. The attribute model analyzes ingredients, preparation steps, and descriptions to self-determine seasonal and holiday associations, eliminating the need for manual metadata entry while comprehensively capturing temporal information across all recipes.
Solution Approach 2:
The patent replaces manual metadata tagging (mechanical process) with automated natural language processing and machine learning (intellectual process). The attribute model uses logistic regression and feature analysis to automatically categorize recipes by temporal attributes, substituting human labor with computational intelligence.
2Loss of information
If subjective descriptions by authors are used to indicate holiday or seasonal associations, then temporal information is provided, but accurately interpreting these descriptions is a difficult natural language processing problem
Solution Approach 1:
The system segments the recipe content into distinct feature categories (ingredients, preparation steps, descriptions) and analyzes each segment separately to infer temporal attributes. This segmentation allows the model to process different types of information through specialized feature extraction methods, improving overall detection accuracy.
Solution Approach 2:
The patent introduces attribute features as intermediaries between the raw recipe content and the temporal attribute classification. These features serve as mediators that translate unstructured text into structured numerical inputs for the logistic regression model, making the natural language processing task more manageable and accurate.
3Loss of time
If recipe posting time is used to determine temporal relevance, then temporal information is available, but the posting time may lead or lag the time period during which a recipe is relevant
Solution Approach 1:
The system performs preliminary analysis of recipe content to infer temporal attributes independently of posting time. By pre-determining the actual temporal association through content analysis before serving the recipe, the system corrects for posting time delays or leads, ensuring accurate temporal matching with user queries.
Data Source
AI summary
Embodiments infer a temporal attribute of a recipe. A recipe is obtained that includes recipe content such as preparation steps and ingredients. A recipe server identifies attribute features in the recipe content, where the attribute features are representative of portions of the recipe content that are correlated with temporal attributes. The recipe server determines whether the recipe is associated with a temporal attribute based on the attribute features and obtained attribute parameters corresponding to the attribute features. A temporal attribute determined to be associated with a recipe is transmitted to a client device in response to a request for the recipe. The estimated temporal attribute is used to filter recipes sent in response to a request for recipes, where the request specifies a criterion based on the temporal attribute. The estimated temporal attribute is used to select recipes for recommendation to a user based on a current time.