scholarly journals Auto-CaseRec: Automatically Selecting and Optimizing Recommendation-Systems Algorithms

2020 ◽  
Author(s):  
Srijan Gupta ◽  
Joeran Beel
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Parameter Tuning ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Automated Algorithm ◽  
Recommendation Algorithms ◽  
Automated Machine Learning ◽  
Human Effort

The advances in the field of Automated Machine Learning (AutoML) have greatly reduced human effort in selecting and optimizing machine learning algorithms. These advances, however, have not yet widely made it to Recommender-Systems libraries. We introduce Auto-CaseRec, a Python framework based on the CaseRec recommender-system library. Auto-CaseRec provides automated algorithm selection and parameter tuning for recommendation algorithms. An initial evaluation of Auto-CaseRec against the baselines shows an average 13.88% improvement in RMSE for theMovielens100K dataset and an average 17.95% improvement in RMSE for the Last.fm dataset.

Recommender System: Towards Classification of Human Intentions in E-Shopping Using Machine Learning

2019 ◽  
Vol 16 (10) ◽  
pp. 4280-4285
Author(s):  
Babaljeet Kaur ◽  
Richa Sharma ◽  
Shalli Rani ◽  
Deepali Gupta
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Machine Learning Algorithms ◽  
Application Domain ◽  
Random Forest Algorithm ◽  
Manual Search ◽  
Online Shoppers ◽  
New Item

Recommender systems were introduced in mid-1990 for assisting the users to choose a correct product from innumerable choices available. The basic concept of a recommender system is to advise a new item or product to the users instead of the manual search, because when user wants to buy a new item, he is confused about which item will suit him better and meet the intended requirements. From google news to netflix and from Instagram to LinkedIn, recommender systems have spread their roots in almost every application domain possible. Now a days, lots of recommender system are available for every field. In this paper, overview of recommender system, recommender approaches, application areas and the challenges of recommender system, is given. Further, we study conduct an experiment on online shoppers’ intention to predict the behavior of shoppers using Machine learning algorithms. Based on the results, it is observed that Random forest algorithm performs the best with 93% ROC value.

scholarly journals A User- Based Recommendation with a Scalable Machine Learning Tool

2015 ◽  
Vol 5 (5) ◽  
pp. 1153 ◽  
Author(s):  
Ch. Veena ◽  
B. Vijaya Babu
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Open Source ◽  
Recommender Systems ◽  
Recommender System ◽  
Historical Data ◽  
Machine Learning Algorithms ◽  
Frame Work ◽  
Machine Learning Tool ◽  
Selection Of

Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

scholarly journals Movie Recommender System Using Machine Learning Algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012052
Author(s):  
Piyush Kumar ◽  
Shaik Golam Kibriya ◽  
Yuva Ajay ◽  
Ilampiray
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

Collaborative Filtering-based Robust Recommender System using Machine Learning Algorithms

2021 ◽  
pp. 1-21
Author(s):  
Utkarsh Pravind ◽  
Palak Porwal ◽  
Abhaya Kumar Sahoo ◽  
Chittaranjan Pradhan
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Deep Learning for Recommender Systems: A Netflix Case Study

AI Magazine ◽  
2022 ◽  
Vol 42 (3) ◽  
pp. 7-18
Author(s):  
Harald Steck ◽  
Linas Baltrunas ◽  
Ehtsham Elahi ◽  
Dawen Liang ◽  
Yves Raimond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommender Systems ◽  
Input Data ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Methods ◽  
Recommendation Algorithms

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

scholarly journals Automated clinical computational biology: an interpretable machine learning framework to predict disease severity and stratify patients from clinical data

2018 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Machine Learning ◽  
Disease Severity ◽  
Clinical Data ◽  
Model Building ◽  
Learning Experience ◽  
Machine Learning Algorithms ◽  
Close Collaboration ◽  
Learning Framework ◽  
Novel Biomarkers ◽  
Automated Machine Learning

We outline an automated computational and machine learning framework that predicts disease severity andstratifies patients. We apply our framework to available clinical data. Our algorithm automatically generatesinsights and predicts disease severity with minimal operator intervention. The computational frameworkpresented here can be used to stratify patients, predict disease severity and propose novel biomarkers fordisease. Insights from machine learning algorithms coupled with clinical data may help guide therapy,personalize treatment and help clinicians understand the change in disease over time. Computationaltechniques like these can be used in translational medicine in close collaboration with clinicians and healthcareproviders. Our models are also interpretable, allowing clinicians with minimal machine learning experience toengage in model building. This work is a step towards automated machine learning in the clinic.

scholarly journals Analyzing the Performance Factors of Machine Learning Algorithms for COVID'19 Data

2020 ◽  
Vol 9 (9) ◽  
pp. 149-155
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Learning Algorithms ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Data Set ◽  
Performance Factors ◽  
Selection Decision

Machine learning is a branch of Artificial intelligence which provides algorithms that can learn from data and improve from experience, without human intervention. Now a day's many of the machine learning algorithms playing a vital role in data analytics. Such algorithms are possible to apply with the recent pandemic COVID situation across the globe. Machine learning algorithms are classified into 3 different groups based on the type of learning process, such as supervised learning, unsupervised learning, and reinforcement learning. By considering the medical observations on the COVID across the globe it has been discussed and concluded to analyze under the supervised learning process. The data set is acquired from the reliable source, it is processed and fed into the classification algorithms. Since learning behaviors are carried out by knowing the input data and expected output data. The data is labeled and has been classified based on labels. In the proposed work, three different algorithms are used to experiment with the COVID'19 dataset and compared for their efficiency and algorithm selection decision is made.

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