Special Issue: Fashion Recommender Systems
Lecture Notes in Social Networks (LNSN) - Springer Journal Volume
Call for papers
Online Fashion retailers have increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. There exists a number of hurdles that customers face with current online shopping solutions. For example, they often feel overwhelmed with the large selection of the assortment and brands. In addition, there is still a lack of effective suggestions capable of satisfying their style preferences. Determining the right size and fit during the purchase journey is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Most importantly, the impact of social networks and influence that fashion influencers have on the choices people make for shopping is undeniable.
The Fashion Recommender Systems book aims to present a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. This is not a college textbook. However, it can be used as a reference text for advanced courses on Cross-domain information retrieval, fashion recommendation algorithms, social network mining and analysis, computer vision and deep learning applications, among numerous others. Through this edited volume, we intend to create a venue to bring together researchers and practitioners from different disciplines, to share, exchange, learn, and develop preliminary results, new concepts, ideas, principles, and methodologies, aiming to advance the area of fashion recommendation.
Read more about the LNSN volume here:
Suggested topics for submissions are (but not limited to):
Computer vision in Fashion (image classification, semantic segmentation, object detection.)
Deep learning in recommendation systems for Fashion.
Learning and application of fashion style (personalized style, implicit and explicit preferences, budget, social behaviour, etc.)
Size and Fit recommendations through mining customers implicit and explicit size and fit preferences.
Modelling articles and brands size and fit similarity.
Usage of ontologies and article metadata in fashion and retail (NLP, social mining, search.)
Addressing cold-start problem both for items and users in fashion recommendation.
Knowledge transfer in multi-domain fashion recommendation systems.
Hybrid recommendations on customers’ history and on-line behavior.
Multi- or Cross- domain recommendations (social media and online shops)
Privacy preserving techniques for customer’s preferences tracing.
Understanding social and psychological factors and impacts of influence on users’ fashion choices (such as Instagram, influencers, etc.)
Authors Submission Due: January 15, 2020
Reviews Submission and Authors Notifications: February 29, 2020
Authors Revisions Due: March 15, 2020
All papers should follow the manuscript preparation guidelines for the Springer Lecture Notes in Social Network Analysis submissions, see Instructions for Authors section at: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.springer.com_series_8768&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=V4fW8YCd1ZGP9_hhcMsX9xlblEUKjF0-wJYumRCbz64&s=aWJdk83UpJ1uh-97xtaCtGfnXdxUfUzlVdvWk1oO2pc&e=
The authors are requested to submit their manuscripts via the online submission manuscript system, available at https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dsifashionxrecsys2019&d=DwIFaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=V4fW8YCd1ZGP9_hhcMsX9xlblEUKjF0-wJYumRCbz64&s=Q8sMudLtfuVbDJWg0ggyYz6ixv2r4kA3Rzd3pTLRJBM&e=
Should there be any further inquiries, please address them to the coordinating guest editor for the special issue at: email@example.com
LNSN volume editor