climate based cloth recommendation using machine
ABSTARCT :
In this paper, we demonstrate a practical system for automatic weather-oriented clothing suggestion, given the weather information, the system can automatically recommend the most suitable clothing from the user s personal clothing album, or intelligently suggest the most pairing one with the userspecified reference clothing.
This is an extremely challenging problem due to the large discrepancy factors that should be considered under different weather conditions. To approach this task, we use clothing attributes as a mid-level bridge to narrow the gap between low-level features and the high-level weather categories.
We adopt a scoring function, which includes three terms, to model the relationship. To acquire an optimized model and verify our proposed method, we collect a large clothing Weather-to-Garment (WoG) dataset.
Experiments on the WoG dataset demonstrate that our learned model are effective for both weather-oriented clothing recommendation and pairing.
EXISTING SYSTEM :
The correlation between weather and purchasing patterns for adult clothing has been studied in several papers such as [6], [7], but little research has been done specifically on the correlation between weather and purchasing patterns for children’s clothing.
Shopping for adult’s clothing and children’s clothing differs in several ways. Adults typically buy most adult clothing for themselves, while children’s clothing is typically bought for the child by her parent(s) or guardian(s).
As children grow, they routinely need larger clothes. For these reasons, it could be reasonable to believe that correlations between weather and purchasing patterns for adult clothing cannot be fully extrapolated to the children’s domain.
Shopping for children’s clothing could be associated with more planning or less spontaneity than shopping for adult clothing.
DISADVANTAGE :
Accuracy Issues: Climate-based clothing recommendations rely on accurate, local weather data. However, weather stations might not always capture real-time or hyper-local variations in climate, especially in large or rural areas.
bjectivity: People's clothing preferences vary significantly, even under similar weather conditions. For example, one person might feel comfortable in a T-shirt at 18°C, while another may prefer a jacket. Machine learning models may struggle to account for these subjective differences.
Personal Data Collection: For a personalized clothing recommendation system to work, it might need access to personal data, such as user preferences, purchase history, or location data. This raises privacy concerns if data is misused or not adequately protected.
Personal Data Collection: For a personalized clothing recommendation system to work, it might need access to personal data, such as user preferences, purchase history, or location data. This raises privacy concerns if data is misused or not adequately protected.
PROPOSED SYSTEM :
The system consists of several integrated modules. First, a Weather Prediction Module uses time-series forecasting and classification algorithms to analyze current and forecasted weather conditions, including temperature, humidity, wind speed, and precipitation, providing accurate environmental data.
This data feeds into the User Profile and Preference Module, which utilizes collaborative filtering and content-based filtering to tailor recommendations to the individual's past clothing choices, preferences, and lifestyle habits.
The Clothing Item Suggestion Module processes the weather and user profile data to recommend specific clothing items, utilizing classification models to suggest attire based on factors such as temperature, wind, and precipitation.
It also considers clothing compatibility and layering, which is handled by the Context-Aware Recommendation Module, ensuring that users are advised on how to adapt their outfits based on factors like time of day, location, and activities. This module can employ techniques such as contextual bandits to make real-time adjustments to the suggestions.
ADVANTAGE :
Tailored to Individual Preferences: AI-based systems can analyze individual preferences, including preferred clothing types, colors, and fabric choices, to provide personalized recommendations based on the current weather conditions.
Instant Decision-Making: Instead of spending time deciding what to wear based on unpredictable weather, a machine recommendation system can offer quick, data-driven suggestions. This is especially useful for people with busy schedules.
Optimal Weather-Appropriate Clothing: AI can suggest clothing that is ideal for the current weather, helping individuals feel more comfortable and avoid the discomfort of being overdressed or underdressed.
Sun Protection: By factoring in UV levels, machine recommendations can suggest clothing that protects from harmful sun exposure, such as recommending hats, sunglasses, or long sleeves when UV levels are high.
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