scholarly journals Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

2019 ◽  
Vol 9 (13) ◽  
pp. 2634 ◽  
Author(s):  
Ok ◽  
Lee ◽  
Kim
Keyword(s):  
Random Walk ◽  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
Online Shopping ◽  
Experimental Studies ◽  
Empirical Evaluation ◽  
Personalized Recommendation ◽  
User Interests ◽  
Sparsity Problem

Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

scholarly journals A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

2019 ◽  
Vol 9 (15) ◽  
pp. 3141
Author(s):  
Li Bai ◽  
Mi Hu ◽  
Yunlong Ma ◽  
Min Liu
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Two Phase ◽  
Group Buying ◽  
Large Scale Data ◽  
User Clustering ◽  
Scale Data

The last two decades have witnessed an explosive growth of e-commerce applications. Existing online recommendation systems for e-commerce applications, particularly group-buying applications, suffer from scalability and data sparsity problems when confronted with exponentially increasing large-scale data. This leads to a poor recommendation effect of traditional collaborative filtering (CF) methods in group-buying applications. In order to address this challenge, this paper proposes a hybrid two-phase recommendation (HTPR) method which consists of offline preparation and online recommendation, combining clustering and collaborative filtering techniques. The user-item category tendency matrix is constructed after clustering items, and then users are clustered to facilitate personalized recommendation where items are generated by collaborative filtering technology. In addition, a parallelized strategy was developed to optimize the recommendation process. Extensive experiments on a real-world dataset were conducted by comparing HTPR with other three recommendation methods: traditional CF, user-clustering based CF, and item-clustering based CF. The experimental results show that the proposed HTPR method is effective and can improve the accuracy of online recommendation systems for group-buying applications.

scholarly journals Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

An Ontology-Based Collaborative Filtering Personalized Recommendation

2012 ◽  
Vol 267 ◽  
pp. 79-82
Author(s):  
Pu Wang
Keyword(s):  
Collaborative Filtering ◽  
Web Sites ◽  
Information Overload ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Collaborative Approach ◽  
The Matrix ◽  
Special Domain ◽  
Sparsity Problem ◽  
The Given

Recommender systems have been successfully used to tackle the problem of information overload, where users of products have too many choices and overwhelming amount of information about each choice. Personalization is widely used in various fields to provide users with more suitable and personalized service. Many e-commerce web sites such as online shop retailers make use of recommendation systems. In order to make recommendations to a user, collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. The paper presents a collaborative filtering personalized recommendation approach based on ontology in the special domain. The method combines ontology technology and item-based collaborative filtering. The given recommendation approach can tackle the traditional recommenders problems, such as matrix sparsity and cold start problems.

scholarly journals Unsupervised Learning for Large Scale Data: The ATHLOS Project

2021 ◽  
Author(s):  
Petros Barmpas ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Panagiotis Anagnostou ◽  
Spiros Georgakopoulos ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Large Scale ◽  
High Dimensional Data ◽  
Experimental Studies ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Types ◽  
Unified Framework

1AbstractRecent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.

scholarly journals Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

2020 ◽  
Author(s):  
Ruobing Xie ◽  
Zhijie Qiu ◽  
Jun Rao ◽  
Yi Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Small Subset ◽  
Multiple Channels ◽  
Attention Network

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

scholarly journals Learning Context-Aware Outfit Recommendation

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 873
Author(s):  
Ahed Abugabah ◽  
Xiaochun Cheng ◽  
Jianfeng Wang
Keyword(s):  
Interior Design ◽  
Visual Information ◽  
Large Scale ◽  
Online Shopping ◽  
Rapid Development ◽  
User Preference ◽  
Learning Context ◽  
Image Region ◽  
User Interests ◽  
Real World Datasets

With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching.

Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment

2021 ◽  
pp. 751-769
Author(s):  
Xinling Tang ◽  
Hongyan Xu ◽  
Yonghong Tan ◽  
Yanjun Gong
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Distributed Storage ◽  
Distributed Processing ◽  
Storage System ◽  
Personalized Recommendation ◽  
Computing Environment ◽  
Cloud Computing Environment

With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.

E-Commerce Collaborative Filtering Algorithm Research Based on Cloud Computing

2013 ◽  
Vol 846-847 ◽  
pp. 1566-1569
Author(s):  
Wen Qing Zhao ◽  
Fei Fei Han ◽  
Rui Cai ◽  
De Wen Wang
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Online Shopping ◽  
Programming Model ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Consumption Data ◽  
Parallel Programming Model ◽  
Collaborative Filtering Algorithm

With the continuous development of online shopping, a day will generate tens of thousands of consumer records. E-commerce sites want to recommend the consumers that they may be interested in the products by analyzing the consumer historical consumption data. However massive consumer records led to recommendation speed getting slow by using the traditional personalized recommendation algorithm. By researching on the collaborative filtering algorithm based on ALS and the MapReduce parallel programming model, we explore parallelization of collaborative filtering algorithm based on ALS. The experimental results show that the algorithm in this paper can improve the computing efficiency.

scholarly journals Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

2016 ◽  
Vol 11 (5) ◽  
pp. 631 ◽  
Author(s):  
Keunho Choi ◽  
Yongmoo Suh ◽  
Donghee Yoo
Keyword(s):  
Collaborative Filtering ◽  
Online Shopping ◽  
Recommendation Systems ◽  
Shopping Mall ◽  
Similarity Function ◽  
Shopping Malls ◽  
Transaction Data ◽  
User Similarity ◽  
Sparsity Problem ◽  
Rating Information

Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.

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