We used fuzzy clustering to generate soft clusters and fuzzify user preferences.

Taaz Photo Makeover Professional And After

Download View publication Copy reference Copy caption Embed figure A) Three samples show the face before, after professional and after our makeup.

B) Comparison of our synthesis results with TAAZ and DailyMakeover.

From top to down, foundation, eye shadow, lipstick, blush, overall effect.

Source publication Examples-Rules Guided Deep Neural Network for Makeup Recommendation Conference Paper Full-text available Feb 2017 Taleb Alashkar Songyao Jiang Shuyang Wang Yun Fu In this paper, we consider fully automatic makeup recommendation and propose a novel examples-rules guided deep neural network approach.

First, makeup-related facial traits are classified into structured coding.

Cite Download full-text Context in source publication Context 1.

Taaz Photo Makeover Movie Recommender Systems

To date, the majority of content-based movie recommender systems (CBMRS).

View An Information Retrieval and Recommendation System for Astronomical Observatories Article Full-text available Mar 2018 Debi Prasanna Acharjya Nikhil Mukund Menon Saurabh Thakur.

Kaustubh Vaghmare We present a machine-learning-based information retrieval system for astronomical observatories that tries to address user-defined queries related to an instrument.

In the modern instrumentation scenario where heterogeneous systems and talents are simultaneously at work, the ability to supply people with the right information helps speed.

Erik Cambria The fast-growing e-commerce scenario brings new challenges to traditional collaborative filtering because the huge amount of users and items requires large storage and efficient recommendation systems.

Hence, hashing for collaborative filtering has attracted increasing attention as binary codes can significantly reduce the storage requirement and m.

View Research on the construction of regional electronic commerce large data analysis platform Article Full-text available Dec 2018 Peng Zhang Abstract The construction of regional electronic commerce large data analysis platform is studied.

First, the collaborative filtering algorithm is focused on the further elaboration.

According to the demand of the recommendation system of the electronic commerce large data analysis platform of a certain agricultural product, the most core part of t.

View Event-Driven Trust Refreshment on Ambient Services Article Mar 2017 Hoang Long Nguyen O-Joun Lee Jai E.

Jung. Hyun-Woo Lee Since trust among entities can change according to various conditions, it is necessary for ambient services to determine when and how the trust has to be updated.

Therefore, our contribution in this study is to present i) a new definition of trust that can be extended to various domains, ii) a novel method based on social events and patterns to tri.

View Citations. Today, in various domains, recommendation systems have become a very useful tool (Alashkar et al.

McAuley 2016, 2015;Niu et al.

Sulthana et al. 2020).

In this study, our focus is on image-based recommender systems and in more particular social images..

An image recommendation technique based on fuzzy inference system Article Full-text available Apr 2021 Somaye Ahmadkhani Mohsen Ebrahimi Moghaddam Nowadays, social network users are facing with a large number of images with the fast development in the social image sharing media.

Since users liking behavior has not a certain manner, it can be modeled by fuzzy logic because fuzzy logic is a decision-making system based on degrees of truth and deals with vague and imprecise information.

Therefore, in the present study, we proposed a fuzzy inference system based on user preferences, consisting of a set of rules according to distinguishing features for each user.

Also together with fuzzy inference system, this method is proposed based on feature extraction and feature selection in order to predict the images which are liked by different users.

In the proposed method, the features from user preferred images are extracted.

Then, feature selection is used to determine the most discriminant features for each user, and accordingly fuzzy inference system is employed to learn user preferences.