Recommendation of complementary garments using ontology
01 Dec 2015-pp 1-4
TL;DR: A novel recommendation engine to suggest coordinated outfits to the users that complements each other that encodes subjective knowledge of clothing experts in Multimedia Web Ontology Language (MOWL) and makes use of evidential and causal reasoning scheme to deal with the media properties of concepts.
Abstract: This paper proposes a novel recommendation engine to suggest coordinated outfits to the users that complements each other. The proposed recommendation model encodes subjective knowledge of clothing experts in Multimedia Web Ontology Language (MOWL) and makes use of evidential and causal reasoning scheme to deal with the media properties of concepts. Our approach automatically identifies the user visual personality and interprets the contextual meaning of media features of the garments in the context of input query image. As a result, personalized complementary garments based on occasion of wear are recommended to the user. We have validated our approach with garment preferences of various models with a large collection of shirts and trousers, collected from various websites.
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28 Aug 2018
TL;DR: An event-based clothing recommender system which uses object detection, which trains a model to identify nine events/scenarios that a user might attend and trains another model to detect clothes out of fifty-three categories of clothes worn at the event.
Abstract: The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommender systems on retail websites generate a lot of this revenue. Thus, improving recommender systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommender system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user.
8 citations
Cites background from "Recommendation of complementary gar..."
...Earlier works like [8] - [10] find clothes that complement one another....
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...Works [8]-[17] all deal with recommendations....
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...Earlier works like [8], [16] and [17] made lexical recommendations....
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01 Jul 2017
TL;DR: A novel multimedia ontology based framework to recommend garments to the users for a specific occasion to form a complement outfit pair, which requires dealing with intrinsic subjectivity involved in modeling the domain.
Abstract: In this work, we propose a novel multimedia ontology based framework to recommend garments to the users for a specific occasion to form a complement outfit pair, which requires dealing with intrinsic subjectivity involved in modeling the domain. The framework automatically derives personal traits of a user such as body color, dimensions from her photograph and garment attributes such as color, pattern from input garment to interpret their respective context. Depending on these contexts, system then recommends outfits that are complementary and satisfactory in terms of reference garment color and user personality using semantic web and image processing techniques. The recommendation model compiles knowledge of clothing in an ontology encoded in Multimedia Web Ontology Language (MOWL) which is capable of describing domain concepts in terms of their media properties and utilizes probabilistic reasoning scheme to reason with them. We have experimented and validated our approach with clothing liking of various models on a collection of garments downloaded from various websites, thus providing an effective garment recommendation interface to the user.
2 citations
Cites background from "Recommendation of complementary gar..."
...The authors in [7] suggested complementary outfits based on certain manual pa rameters and is based on assumption that query garment color and user visual color collectively helps in determining color season user belongs to, lacking mix-and-match color criteria....
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...In contrary, a class of research paper [6] and [7] used bayesian probabilistic approach that enables rea soning with media properties of apparels, user visual appear ance and context of use using MOWL [8]....
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Patent•
23 Feb 2018
TL;DR: Wang et al. as discussed by the authors presented a method and device for clothing personalized design based on a multi-source dynamic knowledge map, which includes the steps of obtaining clothing fashion trends and clothing engineering technical information in real time from unstructured texts of different data sources.
Abstract: The present invention provides a method and device for clothing personalized design based on a multi-source dynamic knowledge map. The method includes the steps of obtaining clothing fashion trends and clothing engineering technical information in real time from unstructured texts of different data sources; generating a clothing design knowledge map from a public knowledge map; integrating the clothing fashion trends, the clothing engineering technical information and the clothing design knowledge map, and constructing a multi-source dynamic clothing design knowledge map that reflects the dynamic changes of the clothing fashion trends and the latest developments in clothing engineering technology; importing the personalized data of a user into the multi-source dynamic clothing design knowledge map to complete the clothing personalized design. The method and device for clothing personalized design based on the multi-source dynamic knowledge map integrates the public knowledge map, real-time information of the clothing fashion trends and the latest development status of the clothing engineering technology with the user personalized data from clothing consumption diversification and personalized demands to shorten the personalized design cycle of the clothing and improve the personalized design quality of the clothing.
1 citations
References
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TL;DR: This research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender system research.
Abstract: Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.
604 citations
"Recommendation of complementary gar..." refers background in this paper
...The prior work on recommendation systems generally employed collaborative filtering where items are recommended based on similar liking’s and purchase behavior of other users [1]....
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29 Oct 2012
TL;DR: 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.
Abstract: In this paper, we aim at a practical system, magic closet, for automatic occasion-oriented clothing recommendation Given a user-input occasion, eg, wedding, shopping or dating, magic closet intelligently suggests the most suitable clothing from the user's own clothing photo album, or automatically pairs the user-specified reference clothing (upper-body or lower-body) with the most suitable one from online shops Two key criteria are explicitly considered for the magic closet system One criterion is to wear properly, eg, compared to suit pants, it is more decent to wear a cocktail dress for a banquet occasion The other criterion is to wear aesthetically, eg, a red T-shirt matches better white pants than green pants To narrow the semantic gap between the low-level features of clothing and the high-level occasion categories, we adopt middle-level clothing attributes (eg, clothing category, color, pattern) as a bridge More specifically, the clothing attributes are treated as latent variables in our proposed latent Support Vector Machine (SVM) based recommendation model The wearing properly criterion is described in the model through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential To learn a generalize-well model and comprehensively evaluate it, we collect a large clothing What-to-Wear (WoW) dataset, and thoroughly annotate the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk Extensive experiments on the WoW dataset demonstrate the effectiveness of the magic closet system for both occasion-oriented clothing recommendation and pairing
287 citations
"Recommendation of complementary gar..." refers background in this paper
...The use of non-trivial knowledge resources and algorithms makes the system incapable to provide recommendations....
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TL;DR: A knowledge framework for garment recommendations, which is based on two pillars that incorporates knowledge about aspects of fashion, such as materials, garments, colours, body types, facial features, social occasion etc., as well as their interrelations, with the purpose of providing personalised recommendations.
Abstract: We propose a knowledge framework for garment recommendations, which is based on two pillars. The first pillar incorporates knowledge about aspects of fashion, such as materials, garments, colours, body types, facial features, social occasion etc., as well as their interrelations, with the purpose of providing personalised recommendations. The said knowledge is encoded in the form of an owl ontology, the origin of which is attributed to fashion experts. Moreover, in commercial fashion sites, customers purchase garments of various types. Because of that, interesting patterns in their purchase behaviour can be sought, and thus groups of garments that tend to be purchased together can be discovered. This forms the second pillar, that can be used to enhance the first pillar with community based garment recommendations. This paper is the description and integration of the aforementioned pillars in a knowledge framework.
29 citations
"Recommendation of complementary gar..." refers background in this paper
...But the system lacked to establish relation between user profile and context of use....
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TL;DR: A new perceptual modeling technique for reasoning with media properties observed in multimedia instances and the latent concepts is proposed, and a probabilistic reasoning scheme for belief propagation across domain concepts through observation of media properties is introduced.
Abstract: Several multimedia applications need to reason with concepts and their media properties in specific domain contexts. Media properties of concepts exhibit some unique characteristics that cannot be dealt with conceptual modeling schemes followed in the existing ontology representation and reasoning schemes. We have proposed a new perceptual modeling technique for reasoning with media properties observed in multimedia instances and the latent concepts. Our knowledge representation scheme uses a causal model of the world where concepts manifest in media properties with uncertainties. We introduce a probabilistic reasoning scheme for belief propagation across domain concepts through observation of media properties. In order to support the perceptual modeling and reasoning paradigm, we propose a new ontology language, Multimedia Web Ontology Language (MOWL). Our primary contribution in this article is to establish the need for the new ontology language and to introduce the semantics of its novel language constructs. We establish the generality of our approach with two disperate knowledge-intensive applications involving reasoning with media properties of concepts.
27 citations
"Recommendation of complementary gar..." refers background or methods in this paper
...To enable reasoning with media properties of garments, user visual personality and context of use, an approach using MOWL [4] has been presented in [6]....
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...MOWL uses causal view of the world and supports mainly two types of entities: concepts, representing the real world entities and the media properties that represent the manifestation of concepts in the media world [4]....
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17 Nov 2013
TL;DR: A novel method for content-based recommendation of media-rich commodities using probabilistic multimedia ontology that enables interpretation of media based and semantic product features in context of domain concepts is presented.
Abstract: We present a novel method for content-based recommendation of media-rich commodities using probabilistic multimedia ontology. The ontology encodes subjective knowledge of experts that enables interpretation of media based and semantic product features in context of domain concepts. Our recommendation is based on semantic compatibility between the products and user profile in context of use. We use probabilistic knowledge representation and reasoning framework to achieve robust and flexible results. The approach has been validated with fashion preferences of several individuals with a large collection of Sarees, an ethnic dress for women in Indian subcontinent.
24 citations
"Recommendation of complementary gar..." refers methods in this paper
...To enable reasoning with media properties of garments, user visual personality and context of use, an approach using MOWL [4] has been presented in [6]....
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