Connected Closet - A Semantically Enriched Mobile Recommender System for Smart Closets

  title={Connected Closet - A Semantically Enriched Mobile Recommender System for Smart Closets},
  author={Anders Lorentzen Kolstad and {\"O}zlem {\"O}zg{\"o}bek and Jon Atle Gulla and Simon Litlehamar},
A common problem for many people is deciding on an outfit from a vastly overloaded wardrobe. [] Key Result Moreover, with the system’s recycling suggestions, the system can be beneficial for the sustainability of the environment and the economy.

Figures and Tables from this paper

Content-Based Recommendations for Sustainable Wardrobes Using Linked Open Data
An Internet of Things system that creates incentives for the users to recycle their clothes, benefiting the environmental sustainability is described and experiments show that the proposed approach outperforms a baseline which does not utilize semantic web technologies.
Context-Aware Recommendations for Sustainable Wardrobes
This paper proposes a content-based recommendation approach that utilizes semantic web technologies and that leverages a set of context signals obtained from the system’s architecture, to recommend clothing items that might be relevant for the user to recycle.
Recommender systems for smart cities
Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits
A novel approach for guiding users in selecting daily fashion out€t recommendations from a system consisting of an Internet of Œings wardrobe enabled with RFID technology and a corresponding mobile application shows promising results in the domain of fashion recommendation.


Using Linked Data to Build Open, Collaborative Recommender Systems
This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborativeRecommender systems.
Recommender Systems - An Introduction
An overview of approaches to developing state-of-the-art recommender systems, including current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches.
Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback
A number of graph-based and matrix factorization recommendation models that jointly exploit user ratings and item metadata are explored, and the results show that the proposed hybrid recommendation models, which exploit rating and semantic data, outperform content- based and collaborative filtering baselines.
A systematic literature review of Linked Data‐based recommender systems
There are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications, including personalization of recommendations, use of more datasets considering the heterogeneity introduced, and creation of new hybrid RS for adding information.
Linked open data to support content-based recommender systems
This paper implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users.
Recommender systems survey
RFID-based user profiling of fashion preferences: blueprint for a smart wardrobe
The experimental results clearly indicate that RFID technology is suitable to aid in creating smart systems.
Hi, magic closet, tell me what to wear!
This paper collects a large clothing What-to-Wear dataset, and thoroughly annotates the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk, to learn a generalize-well model and comprehensively evaluate it.
Linked Data - The Story So Far
The authors describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked data community as it moves forward.
The « Intelligent Wardrobe »
The Institute of Medical Informatics of the Bern University of Applied Sciences has developed a prototype of an intelligent wardrobe that suggests appropriate clothes based on sensor data from the apartment like inside temperature, weather forecast and todays events.