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 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.

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.

An Electronic Commerce Recommender System Based on Product Character

2011 ◽  
Vol 267 ◽  
pp. 909-912 ◽  
Author(s):  
Shen Bao Chen
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Competitive Environment ◽  
Referral Process ◽  
Personalized Service ◽  
Filtering Algorithm ◽  
Customer Churn ◽  
Collaborative Filtering Algorithm

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

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