scholarly journals The Development of E-Commerce Recommendation System Based on Collaborative Filtering

2012 ◽  
Vol 6-7 ◽  
pp. 636-640 ◽  
Author(s):  
Guo Fang Kuang
Keyword(s):  
Experimental Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Product Information ◽  
Data Sets ◽  
User Evaluation ◽  
User Interest ◽  
Target User ◽  
Purchase Process ◽  
Sales Staff

The recommendation system in the e-commerce is to provide customers with product information and recommendations to help customers decide what to buy goods and analog sales staff to recommend merchandise to complete the purchase process. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of E-commerce recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

The Development of Building Materials Recommendation System Based on Collaborative Filtering

2013 ◽  
Vol 281 ◽  
pp. 597-602 ◽  
Author(s):  
Guo Fang Kuang ◽  
Chun Lin Kuang
Keyword(s):  
Collaborative Filtering ◽  
Building Materials ◽  
Recommendation System ◽  
High Strength ◽  
Data Sets ◽  
User Interest ◽  
Insulation Materials ◽  
Target User ◽  
Wide Range ◽  
Materials Used

The building materials used in building materials collectively referred to as building materials. New building materials, including a wide range of insulation materials, insulation materials, high strength materials, breathing material belong to the new material. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of building materials recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items

2020 ◽  
Vol 8 (5) ◽  
pp. 01-15
Author(s):  
Dasong Sun ◽  
Shuqing Li ◽  
Wenjing Yan ◽  
Fusen Jiao ◽  
Junpeng Chen
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Memory Function ◽  
Three Dimensional ◽  
Matrix Decomposition ◽  
Data Sets ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Trust Network ◽  
Experimental Conditions

The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.

scholarly journals MINING TRUST VALUES FROM RECOMMENDATION ERRORS

2006 ◽  
Vol 15 (06) ◽  
pp. 945-962 ◽  
Author(s):  
JOHN O'DONOVAN ◽  
BARRY SMYTH
Keyword(s):  
Collaborative Filtering ◽  
User Satisfaction ◽  
Information Needs ◽  
Recommendation System ◽  
Predictive Accuracy ◽  
Target User ◽  
Fair Comparison ◽  
Collaborative Filtering Algorithm ◽  
History Of ◽  
User Trust

Increasing availability of information has furthered the need for recommender systems across a variety of domains. These systems are designed to tailor each user's information space to suit their particular information needs. Collaborative filtering is a successful and popular technique for producing recommendations based on similarities in users' tastes and opinions. Our work focusses on these similarities and the fact that current techniques for defining which users contribute to recommendation are in need of improvement. In this paper we propose the use of trustworthiness as an improvement to this situation. In particular, we define and empirically test a technique for eliciting trust values for each producer of a recommendation based on that user's history of contributions to recommendations. We compute a recommendation range to present to a target user. This is done by leveraging under/overestimate errors in users' past contributions in the recommendation process. We present three different models to compute this range. Our evaluation shows how this trust-based technique can be easily incorporated into a standard collaborative filtering algorithm and we define a fair comparison in which our technique outperforms a benchmark algorithm in predictive accuracy. We aim to show that the presentation of absolute rating predictions to users is more likely to reduce user trust in the recommendation system than presentation of a range of rating predictions. To evaluate the trust benefits resulting from the transparency of our recommendation range techniques, we carry out user-satisfaction trials on BoozerChoozer, a pub recommendation system. Our user-satisfaction results show that the recommendation range techniques perform up to twice as well as the benchmark.

scholarly journals Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenning Yuan ◽  
Jong Han Lee ◽  
Sai Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Preferences ◽  
Classification Model ◽  
Filtering Algorithm ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Low Dimensional ◽  
Preference Matrix

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

scholarly journals Event driven Recommendation System for E-commerce using Knowledge based Collaborative Filtering Technique

2020 ◽  
Vol 21 (3) ◽  
pp. 369-378
Author(s):  
Mahesh Kumar Singh ◽  
Om Prakash Rishi
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Recommendation System ◽  
Evaluation Studies ◽  
Knowledge Graph ◽  
Knowledge Based ◽  
Target User ◽  
Online Trading ◽  
User Data ◽  
Two Phases

The Internet is changing the method of selling and purchasing items. Nowadays online trading replaces offline trading. The items offered by the online system can influence the nature of buying customers. The recommendation system is one of the basic tools to provide such an environment. Several techniques are used to design and implement the recommendation system. Every recommendation system passes from two phases similarity computation among the users or items and correlation between target user and items. Collaborative filtering is a common technique used for designing such a system. The proposed system uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship  among the users and the items. Almost every existing recommendation system is based on one of feature, review, rating, and popularity of the items in which users’ involvement is very less or none. The proposed approach uses about 100 percent of users’ participation in the form of activities during navigation of the web site. Thus, the system expects under the users’ interest that is beneficial for both seller and buyer. The proposed system relates the category of items, not just specific items that may be interested in the users. We see the effectiveness of this approach in comparison with baseline methods in the area of recommendation system using three parameters precision, recall, and NDCG through online and offline evaluation studies with user data, and its performance is better than all other baseline systems in all aspects.

scholarly journals A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

2019 ◽  
Vol 16 (1(Suppl.)) ◽  
pp. 0263
Author(s):  
AL-Bakri Et al.
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Similarity Measures ◽  
Huge Amount ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Target User ◽  
Global Meaning ◽  
System Memory ◽  
Accuracy Of Prediction

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

scholarly journals Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

2021 ◽  
Author(s):  
Xiangyuan Li ◽  
Fei Ding ◽  
Suju Ren ◽  
Jianmin Bao ◽  
Ruoyu Su ◽  
...  
Keyword(s):  
High Speed ◽  
Recommendation System ◽  
Short Term Memory ◽  
User Behavior ◽  
Data Sets ◽  
Mixed Traffic ◽  
User Interest ◽  
Travel Information ◽  
Lstm Network ◽  
Behavior Characteristics

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.

A Recommendation System with the Use of Comprehensive Trend Indication Based on Weighted Complete Graph

2012 ◽  
Vol 16 (2) ◽  
pp. 266-272
Author(s):  
Takuya Sugimoto ◽  
◽  
Tetsuya Toyota ◽  
Hajime Nobuhara ◽  
Keyword(s):  
Graduate Students ◽  
Collaborative Filtering ◽  
Undergraduate Students ◽  
Complete Graph ◽  
Recommendation System ◽  
Product Information ◽  
Point Of View ◽  
Office Workers ◽  
Internet Shopping ◽  
Typical Method

Recently, Internet shopping has become widespread, websites of which are equipped with a recommendation system to help users easily find their target items from among vast product information. As a typical method to create recommendation information, collaborative filtering is used but it has a problem that recommendation results tend to be biased toward the same category. Since this study intends recommendation with a high discoverability from a large point of view of category, we define dissimilarity between products based on information on Browse Node ID held by some products in Amazon and use k-medoids to newly categorize the products. Moreover, we create a weighted complete graph with those categories as nodes and indicate the trend across different categories. The proposed system estimates and recommends a category strongly related to a category that is thought to be unknown to the user but the user will like based on information of the weighted complete graph. We evaluate the effectiveness of the proposed system through experiments with 9 undergraduate students, 12 graduate students, and 2 office workers as subjects and show that the proposed system is better in recommending unknown products to the user than existing recommendation systems.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

2014 ◽  
Vol 513-517 ◽  
pp. 1878-1881
Author(s):  
Feng Ming Liu ◽  
Hai Xia Li ◽  
Peng Dong
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaofeng Li ◽  
Dong Li
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Algorithm ◽  
Personalized Recommendation ◽  
Filtering Algorithm ◽  
Collaborative Recommendation ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Community Detection Algorithm

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

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