As a class of context-aware systems, context-aware service recommendation aims to bind high-quality services to users while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimentional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our CASR approach.

Context-aware Service Recommendation based on Knowledge Graph Embedding

Customer models for artificial intelligence-based decision support in fashion online retail supply chains

The textile and apparel industries have grown tremendously over the last few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion RS can have a noticeable impact on billions of customers' shopping experiences and increase sales and revenues on the provider side. The goal of this survey is to provide a review of recommender systems that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, explainability, among others) and type of side-information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics.

pdf/a-review-of-modern-fashion-recommender-systems-1etvmnwx.pdf

A Review of Modern Fashion Recommender Systems

The textile and apparel industries have grown tremendously over the past few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms, as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion recommender systems (RSs) can have a noticeable impact on billions of customers’ shopping experiences and increase sales and revenues on the provider side. The goal of this survey is to provide a review of RSs that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, and explainability, among others) and type of side information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics. 

Visual compatibility and virtual feel are critical metrics for fashion analysis yet are missing in existing fashion designs and platforms. An explicit model is much needed for implanting visual compatibility through fashion image inpainting and virtual try-on. With rapid advancements in the Computer Vision realm, the increase in creating customer experience which leads to the great potential of interest to retailers and customers. The public datasets available are very much fit for generating outfits from Generative Adversarial Networks (GANs) but the custom outfits of the users themselves lead to low accuracy levels. This work is the first step in analyzing and experimenting with the fit of custom outfits and visualizing it to the users on them which creates the great customer experience. The work analyses the need for providing visualization of custom outfits on users in the large corpora of AI in Fashion. The authors propose a novel architecture which facilitates the combining outfits provided by the retailers and visualize it on the users themselves using Neural Body Fit. This work creates a benchmark in disentangling the custom generation of cloth outfits using GANs and virtually trying it on the users to ensure a virtual-photorealistic appearance and results to create a great customer experience by using AI. Extensive experiments show the high accuracy levels on custom outfits generated by GANs but not in customized levels. This experiment creates new state-of-art results by plotting users pose for calculating the lengths of each body-part segment (hand, leg, and so forth), segmentation + NBF for accurate fitting of the cloth outfit. This paper is different from all other competitors in terms of approach for the virtual try-on for creating a new customer experience.

/pdf/fashionfit-analysis-of-mapping-3d-pose-and-neural-body-fit-40mhgx73gd.pdf

FashionFit: Analysis of Mapping 3D Pose and Neural Body Fit for Custom Virtual Try-On

E-commerce recommender systems (RSs) can help users quickly find what they need or new products they might be interested in. To continuously enhance user trust in the website, improve page visits and dwell time, and most importantly, increase gross merchandise value (GMV), it is crucial to understand and capture the important information hidden in the data, which has a great impact on user choice. The fashion e-commerce websites can collect the attributes of items and users as well as the user purchase behaviors, but lack the fine-grained classification of the items and the implicit relationship between items and users. This paper focuses on Amazon fashion dataset, one of the most widely used datasets in the fashion field. A differentiated recommendation framework is proposed that provides different recommendation paths for active and inactive users to improve the overall recommendation quality. In the framework, a data augmentation algorithm based on transfer learning is proposed to filter out the irrelevant items and label items with fine-grained tags, and a user-item knowledge graph is built to discover the potential relationship between items and users. Finally, a differentiated recommendation strategy is put forward to make different recommendations for users with different characteristics. The experimental results show that through data augmentation algorithm to improve data quality, factorization machine model produces higher recommendation accuracy, the constructed knowledge graph can alleviate the cold start problem for recommendation, and the differentiated recommendation strategy has achieved better recommendations for both active and inactive users.

/pdf/differentiated-fashion-recommendation-using-knowledge-graph-b8mkfw4q45.pdf

Differentiated Fashion Recommendation Using Knowledge Graph and Data Augmentation

Graph Convolutional Networks (GCNs) are popular for learning representation of graph data and have a wide range of applications in social networks, recommendation systems, etc. However, training GCN models for large networks is resource intensive and time consuming, which hinders them from real deployment. The existing GCN training methods intended to optimize the sampling of mini-batches for stochastic gradient descent to accelerate training process, which did not reduce the problem size and had limited reduction in computation complexity. In this paper, we argue that a GCN can be trained with a sampled subgraph to produce approximate node representations, which inspires us a novel perspective to accelerate GCN training via network sampling. To this end, we propose a label-centric cumulative sampling (LCS) framework for training GCNs for large graphs. The proposed method constructs a subgraph cumulatively based on probabilistic sampling, and trains the GCN model iteratively to generate approximate node representations. The optimality of LCS is theoretically guaranteed to minimize the bias during node aggregation procedure in GCN training. Extensive experiments based on four real-world network datasets show that the LCS framework accelerates the training for the state-of-the-art GCN models up to 17x without causing noteworthy model accuracy drop.

Resource-Efficient Training for Large Graph Convolutional Networks with Label-Centric Cumulative Sampling

Graph stream data is widely applied to describe the relationships in networks such as social networks, computer networks and hyperlink networks. Due to the large volume and high dynamicity of graph streams, several graph sketches were proposed to summarize them for fast queries. However, the existing graph sketches suffer from low performance on graph query tasks due to hash collisions between heavy and light edges. In this paper, we propose a novel learning-based Dichotomy Graph Sketch (DGS) mechanism, which adopts two separate graph sketches, a heavy sketch and a light sketch, to store heavy edges and light edges respectively. DGS periodically obtains heavy edges and light edges in a session of a graph stream, and use them as training samples to train a deep neural network (DNN) based binary classifier. The DNN-based classifier is then utilized to decide whether the upcoming edges are heavy or not, and store them in different graph sketches accordingly. With the learnable classifier and the dichotomy graph sketches, the proposed mechanism can resolve the hashing collision problem and significantly improve the accuracy for graph query tasks. We conducted extensive experiments on three real-world graph stream datasets, which show that DGS outperforms the state-of-the-art graph sketches in a variety of graph query tasks. 

Learning-Based Dichotomy Graph Sketch for Summarizing Graph Streams with High Accuracy

Graph meta learning aims to learn historical knowledge from training graph neural networks (GNNs) models and adapt it to downstream learning tasks in a target graph, which has drawn increasing attention due to its ability of knowledge transfer and fast adaptation. While existing graph meta learning approaches assume the learning tasks are from the same graph domain but lack the solution for multi-domain adaptation. In this paper, we address the multi-domain generalized graph meta learning problem, which is challenging due to non-Euclidean data, inequivalent feature spaces, and heterogeneous distributions. To this end, we propose a novel solution called MD-Gram for multi-domain graph generalization. It introduces an empirical graph generalization method that uses empirical vectors to form a unified expression of non-Euclidean graph data. Then it proposes a multi-domain graphs transformation approach to transform the learning tasks from multiple source-domain graphs with inequivalent feature spaces into a common domain, where graph meta learning is conducted to learn generalized knowledge. It further adopts a domain-specific GNN enhancement method to learn a customized GNN model to achieve fast adaptation in the unseen target domain. Extensive experiments based on four real-world graph domain datasets show that the proposed method significantly outperforms the state-of-the-art in multi-domain graph meta learning tasks.

/pdf/multi-domain-generalized-graph-meta-learning-jk2po9q3.pdf

Multi-Domain Generalized Graph Meta Learning

In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets 2650 times smaller.1

/pdf/clustered-embedding-learning-for-recommender-systems-1e8fvuv2.pdf

Clustered Embedding Learning for Recommender Systems

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