PurposeWith the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers' economic models to help drive online sales. Initially, this paper undertakes an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this article reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps, and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approachThis study utilizes empirical research drawing on 130 academic publications indexed from online Databases. We introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications, and keywords. FindingsThis study classified high-tech integrated apparel systems into 3D CAD systems, personalized design systems, and recommendation systems. Our research interest is focused on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms were tested in system modelling, existing research lacks of concerns in terms of apparel and users profiles research. Thus, from the review, we have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/valueBased on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

Background: To systematically review the efficacy of surgical versus nonsurgical treatment for acute patellar dislocation. Materials and Methods: PubMed, Cochrane, and Embase were searched up to February 12, 2019. After removing duplicates, preliminary screening, and reading the full texts, we finally selected 16 articles, including 11 randomized controlled trials and 5 cohort studies. The quality of the enrolled studies was evaluated by Jadad score or Newcastle–Ottawa scale. Meta-analyses were performed using odds ratio (OR) and standardized mean difference (SMD) as effect variables. The clinical parameters assessed included mean Kujala score, rate of redislocation, incidence of patellar subluxation, patient satisfaction, and visual analog scale (VAS) for pain. Evidence levels were determined using GRADE profile. Results: The 16 included studies involved 918 cases, 418 in the surgical group and 500 in the nonsurgical group. The results of the meta-analysis showed higher mean Kujala score (SMD = 0.79, 95% confidence interval [CI] [0.3, 1.28], P = .002) and lower rate of redislocation (OR = 0.44, 95% CI [0.3, 0.63], P < .00001) in the surgical group than the nonsurgical group, but showed insignificant differences in the incidence of patellar subluxation (OR = 0.61, 95% CI [0.36, 1.03], P = .06), satisfaction of patients (OR = 1.44, 95% CI [0.64, 3.25], P = .38), and VAS (SMD = 0.84, 95% CI [−0.36, 9.03], P = .84). Conclusion: For patients with primary acute patellar dislocation, surgical treatment produces a higher mean Kujala score and a lower rate of redislocation than nonsurgical treatment.

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people and know what to learn. This paper aims to explore an advanced apparel style learning and recommendation system that can recognise deep designassociated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert. Design/methodology/approach This study first proposes a new clothes style training data. Secondly, it designs three intelligent apparel learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models' performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a Convolutional Neural Network joint with the baseline classifier Support Vector Machine and the other is with a newly proposed classifier Later Kernel Fusion. Findings The results show that the most accurate model (with average prediction rate of 88.1%) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that (1) adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5%, Model B to 86%, Model C), and (2) the new concept of apparel recommendation based on style meanings is technically applicable. Originality/value The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

3D body scanning technology opens opportunities for virtual try-on and automatic made-to-measure apparel design. This paper proposes a new feature-based parametric method for modeling human body shape from scanned point clouds of a 3D body scanner [TC] 2. The human body model consists of two layers: the skeleton and the cross-sections of each body part. Firstly, a simple skeleton model from the body scanner [TC] 2 system has been improved by adding and adjusting the position of joints in order to better address some fit issues related to body shape changes such as spinal bending. Secondly, an automatic approach to extracting semantic features for cross-sections has been developed based on the body hierarchy. For each cross-section, it is described by a set of key points which can be fit with a closed cardinal spline. According to the point distribution in point clouds, an extraction method of key points on cross-sections has been studied and developed. Thirdly, this paper presents an interpolation approach to fitting the key points on a cross section to a cardinal spline, in which different tension parameters are tested and optimized to represent simple deformations of body shape. Finally, a connection approach of body parts is proposed by sharing a boundary curve. The proposed method has been tested with the developed virtual human model (VHM) system which is robust and easier to use. The model can also be imported in a CAD environment for other applications. Note to Practitioners-Automatic made-to-measure for mass customization has become one of the important developments for the apparel industry. 3D body scanning technologies are used to capture a virtual clone of an individual human body model for mass customization. However, these methods have failed to overcome two critical problems. First, the extremely expensive cost of the equipment prevents it from wide applications in garment industry. Second, the data format either in the original point clouds or simplified mesh models is not easily to be linked to a parametric model, which can be automatically modified to different shapes by the user parameter inputs. For this reason, this paper intended to build a parametric human body model, which can represent an individual body by inputting some key