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 

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 

  *   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.)


Import dates


Authors Submission Due: January 15, 2020

Reviews Submission and Authors Notifications: February 29, 2020

Authors Revisions Due: March 15, 2020


Submission guidelines


All papers should follow the manuscript preparation guidelines for the Springer 
Lecture Notes in Social Network Analysis submissions, see Instructions for 
Authors section at: http://www.springer.com/series/8768

The authors are requested to submit their manuscripts via the online submission 
manuscript system, available at 

Should there be any further inquiries, please address them to the coordinating 
guest editor for the special issue at: fashionrecsysb...@gmail.com

Best wishes,

Nima Dokoohaki

LNSN volume editor??

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