scholarly journals Personalized Recommender System using Collaborative Filtering Technique and Pyramid Maintenance Algorithm

2016 ◽  
Vol 136 (8) ◽  
pp. 25-31 ◽  
Author(s):  
Minakshi Pachpatil ◽  
Anjana N.
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Filtering Technique ◽  
Personalized Recommender System

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.

Providing Personalized Services to Users in a Recommender System

2015 ◽  
Vol 10 (2) ◽  
pp. 26-48 ◽  
Author(s):  
Olukunle Oduwobi ◽  
Bolanle Adefowoke Ojokoh
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Ability ◽  
Learning Material ◽  
Electronic Library ◽  
Learning Materials ◽  
Personalized Services ◽  
E Learning ◽  
Personalized Recommender System ◽  
Content Based Filtering

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

scholarly journals A Research on Similarity Measure to Identify Effective Similar Users in Recommender Systems

2019 ◽  
Vol 8 (9S2) ◽  
pp. 834-840
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Drastic Increase ◽  
Measure Model ◽  
Filtering Technique ◽  
Selection Of

In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user

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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