A Collaborative Filtering Recommendation Approach Based on User Rating Similarity and User Attribute Similarity

2013 ◽  
Vol 846-847 ◽  
pp. 1736-1739
Author(s):  
Feng Ge
Keyword(s):  
Information Technology ◽  
Marketing Strategy ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Information Overload ◽  
Information Resources ◽  
Recommendation Algorithm ◽  
Network Information ◽  
Usable Information ◽  
Rating Information

With the speedy development of network, information technology has provided an unmatched amount of information resources. It has also led to the problem of information overload. However, people experiences and knowledge often do not enough to process the vast amount of usable information. Thus, approaches to help find resources of interest have attracted much attention from researchers. And recommender systems have arrived to solve this problem. Recommender system plays an important role mainly in an electronic commerce environment as a new marketing strategy. Although a varied of recommendation techniques has been developed recently, collaborative filtering has been known to be the most successful recommendation techniques and has been used in a number of different applications. But traditional collaborative filtering recommendation algorithm has the problem of sparsity. Aiming at the problem of data sparsity for personalized filtering systems, a collaborative filtering recommendation algorithm based on user rating similarity and user attribute similarity is given. This approach not only considers the user item rating information, but also takes into account the user attribute.

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 Enhanced Hybrid Recommender System using Social Friend Network

2014 ◽  
Vol 10 (4) ◽  
pp. 2023-2031
Author(s):  
Shalmali A. Patil ◽  
Reena Pagare
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Recommender System ◽  
Social Relationship ◽  
Information Overload ◽  
Great Success ◽  
Traditional System ◽  
Use Of Data ◽  
The Social ◽  
Hybrid Recommender

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

Pre-Filling Collaborative Filtering Algorithm Based on Matrix Factorization

2013 ◽  
Vol 411-414 ◽  
pp. 2223-2228
Author(s):  
Dong Liang Su ◽  
Zhi Ming Cui ◽  
Jian Wu ◽  
Peng Peng Zhao
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
User Profile ◽  
Personalized Recommendation ◽  
System Quality ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Recommender System

Nowadays personalized recommendation algorithm of e-commerce can hardly meet the needs of users as an ever-increasing number of users and items in personalized recommender system has brought about sparsity of user-item rating matrix and the emergence of more and more new users has threatened recommender system quality. This paper puts forward a pre-filled collaborative filtering recommendation algorithm based on matrix factorization, pre-filling user-item matrixes by matrix factorization and building nearest-neighbor models according to new user profile information, thus mitigating the influence of matrix sparsity and new users and improving the accuracy of recommender system. The experimental results suggest that this algorithm is more precise and effective than the traditional one under the condition of extremely sparse user-item rating matrix.

scholarly journals A Triangular Personalized Recommendation Algorithm for Improving Diversity

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Cai ◽  
Xiaowang Yang ◽  
Yusheng Huang ◽  
Hongjun Li ◽  
Qiang Sang
Keyword(s):  
Collaborative Filtering ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Method ◽  
Online Databases ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy ◽  
Third Dimension ◽  
Triangular Area

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
Author(s):  
WEI ZENG ◽  
MING-SHENG SHANG ◽  
QIAN-MING ZHANG ◽  
LINYUAN LÜ ◽  
TAO ZHOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
High Diversity ◽  
Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

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 Recommending Ads from Trustworthy Relationships in Pervasive Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Francisco Martinez-Pabon ◽  
Juan Camilo Ospina-Quintero ◽  
Gustavo Ramirez-Gonzalez ◽  
Mario Munoz-Organero
Keyword(s):  
Collaborative Filtering ◽  
Pervasive Computing ◽  
Recommender System ◽  
Contextual Information ◽  
Cognitive Effort ◽  
Small Companies ◽  
Inference Process ◽  
Recommendation Algorithm ◽  
Novel Approach ◽  
Trust Inference

The use of pervasive computing technologies for advertising purposes is an interesting emergent field for large, medium, and small companies. Although recommender systems have been a traditional solution to decrease users’ cognitive effort to find good and personalized items, the classic collaborative filtering needs to include contextual information to be more effective. The inclusion of users’ social context information in the recommendation algorithm, specifically trust in other users, may be a mechanism for obtaining ads’ influence from other users in their closest social circle. However, there is no consensus about the variables to use during the trust inference process, and its integration into a classic collaborative filtering recommender system deserves a deeper research. On the other hand, the pervasive advertising domain demands a recommender system evaluation from a novelty/precision perspective. The improvement of the precision/novelty balance is not only a matter related to the recommendation algorithm itself but also a better recommendations’ display strategy. In this paper, we propose a novel approach for a collaborative filtering recommender system based on trust, which was tested throughout a digital signage prototype using a multiscreen scheme for recommendations delivery to evaluate our proposal using a novelty/precision approach.

Analysis of Chinese Word Segmentation Technology

2014 ◽  
Vol 687-691 ◽  
pp. 1540-1543 ◽  
Author(s):  
Ke Zhu
Keyword(s):  
Information Technology ◽  
Information Society ◽  
Word Segmentation ◽  
Information Resources ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Network Information ◽  
Hybrid Techniques

Under the influence of network information resources in exponentially growing, we are entering the information society, and all aspects of our lives has permeated with information technology. But the Chinese word segmentation technology for processing Chinese information becomes more and more important. Therefore, this paper sets out a series of Chinese word segmentation techniques, which mainly consists of Chinese word segmentation technology based on statistic, Chinese word segmentation techniques based on dictionary and hybrid techniques of Chinese word segmentation and segmentation technology based on knowledge and understanding.

scholarly journals THE RECOMMENDATION ALGORITHM FOR AN ONLINE ART GALLERY

2018 ◽  
Vol 7 (2) ◽  
pp. 108-119
Author(s):  
Waldemar Karwowski ◽  
Marian Rusek ◽  
Joanna Sosnowska
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation Systems ◽  
User Profile ◽  
Art Gallery ◽  
Recommendation Algorithm ◽  
Design And Implementation ◽  
Filtering Technique

The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.

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