C2C E-Commerce Recommender System Based on Three-Dimensional Collaborative Filtering

2013 ◽  
Vol 336-338 ◽  
pp. 2563-2566
Author(s):  
Dan Xiang Ai ◽  
Hui Zuo ◽  
Jun Yang
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Rating Data ◽  
Filtering Method ◽  
Sparsity Problem

To solve the special recommendation problem in C2C e-commerce websites, a three-dimensional collaborative filtering recommendation method which can recommend seller and product combinations is proposed by extending the traditional two-dimensional collaborative filtering method. And a C2C e-commerce recommender system based on the proposed method is designed. The framework of the system and the key calculations in the recommendation process are discussed. The system firstly calculates seller similarities using seller features, and fills the rating set based on sales relations and seller similarities to solve the sparsity problem of the three-dimensional rating data. Then it calculates the buyer similarities using historical ratings, decides neighbors and predicts unknown ratings. Finally it recommends the seller and product combinations with the highest prediction ratings to the target buyer. A true data experiment proves the good recommendation performance of the system.

A Novel Three-Dimensional Recommendation Approach for C2C E-Commerce Platform

2013 ◽  
Vol 717 ◽  
pp. 714-719
Author(s):  
Dan Xiang Ai ◽  
Hui Zuo ◽  
Jun Yang
Keyword(s):  
Collaborative Filtering ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Dimensional Approach ◽  
Inference Mechanism ◽  
Data Sparsity ◽  
Filtering Method ◽  
Content Based Filtering

The recommendation problem in C2C e-commerce platform is three-dimensional, for it involves three entities: buyers, products and sellers. Traditional two-dimensional recommendation methods used in B2C websites are not applicable for the new task of recommending seller and product combinations to the target buyer in C2C websites. We formally defined the recommendation problem in C2C, and proposed a three-dimensional approach by compounding and extending collaborative filtering and content-based filtering method. In the proposed approach, buyer similarity and seller similarity are measured to model the buyers personalized preferences, and a rating inference mechanism is employed to reduce the data sparsity caused by multi-dimensions. The model and the key calculations of the approach are discussed. And an example of calculation is demonstrated.

scholarly journals Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

2021 ◽  
Vol 11 (24) ◽  
pp. 11890
Author(s):  
Silvana Vanesa Aciar ◽  
Ramón Fabregat ◽  
Teodor Jové ◽  
Gabriela Aciar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
User Experiences ◽  
Relevant Feedback ◽  
Filtering Method

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.

Combining Item Rating Similarity and Item Classification Similarity for Better Recommendation Quality

2012 ◽  
Vol 461 ◽  
pp. 289-292
Author(s):  
Kai Zhou
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Fundamental Problem ◽  
The Other ◽  
Recommendation Algorithm ◽  
Data Sparsity ◽  
Filtering Method ◽  
Product Recommendation ◽  
Sparsity Problem

Recommender systems are becoming increasingly popular, and collaborative filtering method is one of the most important technologies in recommender systems. The ability of recommender systems to make correct predictions is fundamentally determined by the quality and fittingness of the collaborative filtering that implements them. It is currently mainly used for business purposes such as product recommendation. Collaborative filtering has two types. One is user based collaborative filtering using the similarity between users to predict and the other is item based collaborative filtering using the similarity between items. Although both of them are successfully applied in wide regions, they suffer from a fundamental problem of data sparsity. This paper gives a personalized collaborative filtering recommendation algorithm combining the item rating similarity and the item classification similarity. This method can alleviate the data sparsity problem in the recommender systems

scholarly journals Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

2019 ◽  
Vol 2 (3) ◽  
pp. 334
Author(s):  
Imam Fahrurrozi ◽  
Estu Muh Dwi Admoko ◽  
Anang Susilo
Keyword(s):  
Collaborative Filtering ◽  
Semantic Similarity ◽  
Recommender System ◽  
Cold Start ◽  
Performance Tests ◽  
Online Commerce ◽  
Filtering Method ◽  
Similarity Method

Recommender system is a component which has been developed for online commerce purposes. In this issue, one of the popular methods that has been widely used is collaborative filtering. However, this method has some drawbacks and needs to be improved. Therefore, in this research a combination of Collaborative Filtering (CF) and semantic similarity method has been compare with original CF, and the result expected reducing some deficiencies on the original collaborative filtering method. Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue.

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

2020 ◽  
Vol 19 (02) ◽  
pp. 385-412 ◽  
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Least Square ◽  
Data Sparsity ◽  
Filtering Method ◽  
Sparsity Problem ◽  
Hybrid Collaborative Filtering ◽  
User Ratings ◽  
Hybrid Recommendation

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

Enhance the Quality of Collaborative Filtering using Tagging

2020 ◽  
Vol 13 ◽  
Author(s):  
Latha Banda ◽  
Karan Singh
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Collaborative Tagging ◽  
Rating Data ◽  
Missing Value ◽  
Value Prediction ◽  
Sparsity Problem ◽  
Missing Value Prediction ◽  
Weight Model

Background: Due to enormous data in web sites, recommending users for every item is impossible. For this problem Recommender Systems (RS) are introduced. RS is categorized into content-based (CB), collaborative Filtering (CF) and Hybrid RS. Based on these techniques recommendations are done to user. In this, CF is the recent technique used in RS in which tagging features also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues here we have proposed Tag and Time weight model with GA in Collaborative Tagging. Method: Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of missing value prediction. Here in this the sparsity problem can be removed using missing value prediction and cold start problems are removed using tag and time weight model using GA. Results: Here we have compared the results of Collaborative Filtering with cosine similarity (CF-CS), Collaborative Filtering with Diffusion Similarity (CF-DS), Tag and Time weight model with Diffusion similarity (TAW-TIW-DS) and Tag and Time weight model using Diffusion similarity and Genetic algorithm (TAW-TIW-DS-GA). Conclusion: Here we have compare the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Hit-rate and Hit-rank. Based on these metrics for every split TAW-TIW-DS-GA shown best results as compared to existing approach.

scholarly journals Recommendation System Using Autoencoders

2020 ◽  
Vol 10 (16) ◽  
pp. 5510 ◽  
Author(s):  
Diana Ferreira ◽  
Sofia Silva ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Singular Value ◽  
High Dimensions ◽  
Data Sparsity ◽  
Filtering Method ◽  
Squared Error ◽  
Sparsity Problem ◽  
Value Decomposition

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

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