scholarly journals Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hansaim Lim ◽  
Paul Gray ◽  
Lei Xie ◽  
Aleksandar Poleksic
Keyword(s):  
Virtual Screening ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Genome Scale ◽  
Cold Start Problem ◽  
Filtering Approach

scholarly journals A collaborative filtering approach to mitigate the new user cold start problem

2012 ◽  
Vol 26 ◽  
pp. 225-238 ◽  
Author(s):  
Jesús Bobadilla ◽  
Fernando Ortega ◽  
Antonio Hernando ◽  
Jesús Bernal
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Cold Start Problem ◽  
Filtering Approach

P2PCF: A collaborative filtering based recommender system for peer to peer social networks

2021 ◽  
pp. 1-19
Author(s):  
Lyes Badis ◽  
Mourad Amad ◽  
Djamil Aïssani ◽  
Sofiane Abbar
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Peer To Peer ◽  
Public Concerns ◽  
Cold Start Problem ◽  
Filtering Approach

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage.

scholarly journals Discrete Deep Learning Based Collaborative Filtering Approach for Cold Start Problem

2019 ◽  
Vol 12 (3) ◽  
pp. 68-75
Author(s):  
Archana Kalidindi ◽  
◽  
Prasanthi Yavanamandha ◽  
Anusha Kunuku ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Cold Start Problem ◽  
Filtering Approach

Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem

2021 ◽  
pp. 1492-1512
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L.D.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

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