Proceedings ArticleDOI
A Collaborative Filtering based Recommender System for Suggesting New Trends in Any Domain of Research
M Viswa Murali,T G Vishnu,Nancy Victor +2 more
- pp 550-553
TLDR
A research-paper recommender system using collaborative filtering approach to recommend a user with best research papers in their domain according to their queries and based on the similarities found from other users on the basis of their queries, which will help in avoiding time consuming searches for the user.Abstract:
Recommender system, an information filtering technology used in many items is presented in web sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media in general. In today’s world, time has more value and the researchers have no much time to spend on searching for the right articles according to their research domain. More than 250 research paper recommender systems were published and the quantity of research papers published every day is increasing rapidly. Thus it needs an efficient searching and filtering mechanism to choose the quality research papers, so that the effort and time of researchers can be saved. The recommender system proposed here uses three major factors used for building this system which includes datasets, prediction rating based on users and cosine similarity. The ratings are made by user which will be determined by the number of accurate ratings they provide. The results are then sorted by using cosine similarity. We propose a research-paper recommender system using collaborative filtering approach to recommend a user with best research papers in their domain according to their queries and based on the similarities found from other users on the basis of their queries, which will help in avoiding time consuming searches for the user.read more
Citations
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Book ChapterDOI
Investigating Graph-Based Recommendations Systems and Graph Traversal Algorithms
TL;DR: In this article , the authors used collaborative filtering-based recommendation system along with graph-based learning techniques like Gensim Word2vec and GraphSage to get the relationship between the users and products.
Journal ArticleDOI
Trends and Techniques used in Tourist Recommender System : A Review
TL;DR: In this paper , the authors review several trends and techniques currently being used in tourist recommender systems, including three different types: content-based filtering, collaborative filtering, and hybrid filtering.
Proceedings ArticleDOI
Automatic Employability Test for Factory Workers using Collaborative Filtering
Ahona Ghosh,Sriparna Saha +1 more
TL;DR: In this paper, an automatic employability test platform using collaborative filtering where similar and best matched activities have been recognized first by the use of item-based-collaborative filtering and then based on the performance of similar activities done by similar subjects, the ranking of employability has been determined for unknown workers using user-based collaborative filtering.
Book ChapterDOI
A Smart Movie Recommendation System Using Machine Learning Predictive Analysis
TL;DR: In this paper , a Python-based machine learning predictive analysis-based intelligent movie recommendation system (RS) was proposed, which uses the correlation between numbers of factors to obtain precise results.
Proceedings ArticleDOI
Best-Fit: Best Fit Employee Recommendation
TL;DR: In this paper , an architecture has been proposed in which uses Random Forest, SVM, Decision Tree classifiers and similarity techniques to find the closest employees suitable for the vacancy, which includes finding similarities in the skills, qualifications and experience.
References
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Proceedings ArticleDOI
Improving recommendation lists through topic diversification
TL;DR: This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists.
Journal ArticleDOI
Research-paper recommender systems: a literature survey
TL;DR: Several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.
Book ChapterDOI
Finding relevant papers based on citation relations
Yicong Liang,Qing Li,Tieyun Qian +2 more
TL;DR: A novel method to address the problem of recommending relevant papers to researchers by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size is proposed.
Book ChapterDOI
Research paper recommender systems: a subspace clustering approach
TL;DR: A scalable subspace clustering algorithm (SCuBA) that tackles the large number of dimensions and the sparseness of the data in the research paper domain and performs better than the traditional collaborative filtering approaches when recommending research papers.
Journal ArticleDOI
A Movie Recommender System: MOVREC
TL;DR: A movie recommendation system based on collaborative filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time.