Improved Collaborative Filtering Method Applied in Movie Recommender System

2012 ◽  
pp. 427-432
Author(s):  
Tian Liang ◽  
Shunxiang Wu ◽  
Da Cao
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Filtering Method

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.

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.

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.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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