A Public Cloud Based SOA Workflow for Machine Learning Based Recommendation Algorithms

2015 ◽  
Author(s):  
Ram G. Athreya ◽  
Srinivasan Thanukrishnan
Keyword(s):  
Machine Learning ◽  
Public Cloud ◽  
Recommendation Algorithms

scholarly journals The algorithm and the flow: Netflix, machine learning and recommendation algorithms

Intexto ◽  
2019 ◽  
pp. 166-184
Author(s):  
João Damasceno Martins Ladeira
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Cultural Impact ◽  
Recommendation Algorithms ◽  
Definition Of

This article discusses the Netflix recommendation system, expecting to understand these techniques as a part of the contemporary strategies for the reorganization of television and audiovisual. It renders problematic a technology indispensable to these suggestions: the tools for artificial intelligence, expecting to infer questions of cultural impact inscribed in this technique. These recommendations will be analyzed in their relationship with the formerly decisive form for the constitution of broadcast: the television flow. The text investigates the meaning such influential tools at the definition of a television based on the manipulation of collections, and not in the predetermined programming, decided previously to the transmission of content. The conclusion explores the consequences of these archives, which concedes to the user a sensation of choice in tension with the mechanical character of those images.

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 Research and Application of Film and Television Literature Recommendation Based on Secure Internet of Things and Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jieqiong Zhou ◽  
Zhenhua Wei ◽  
Bin Peng ◽  
Fangchun Chi
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
User Preferences ◽  
Learning Technology ◽  
Film And Television ◽  
Pmf Model ◽  
Recommendation Algorithms ◽  
Learning Reinforcement ◽  
Public Datasets ◽  
The Internet Of Things

Film and television literature recommendation is an AI algorithm that recommends related content according to user preferences and records. The wide application in various APPs and websites provides users with great convenience. This article aims to study the Internet of Things and machine learning technology, combining deep learning, reinforcement learning, and recommendation algorithms, to achieve accurate recommendation of film and television literature. This paper proposes to use the ConvMF-KNN recommendation model to verify and analyze the four models of PMF, ConvM, ConvMF-word2vec, and ConvMF-KNN, respectively, on public datasets. Using the path information between vertices in bipartite graph and considering the degree of vertices, the similarity between items is calculated, and the neighbor item set of items is obtained. The experimental results show that the ConvMF-KNN model combined with the KNN idea effectively improves the recommendation accuracy. Compared with the accuracy of the PMF model on the MovieLens 100 k, MovieLens 1 M, and AIV datasets, the accuracy of the ConvMF model on the above three datasets is 5.26%, 6.31%, and 26.71%, respectively, an increase of 2.26%, 1.22%, and 7.96%. This model is of great significance.

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.

Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online Work

2021 ◽  
Author(s):  
Marios Kokkodis
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Reputation Systems ◽  
Informed Decisions ◽  
Learning Techniques ◽  
Recommendation Algorithms ◽  
Different Types ◽  
Different Dimensions ◽  
Online Labor ◽  
Quality Assessments

Current reputation systems in online (labor) markets are overly positive and unidimensional. This article presents a new reputation framework that combines human input with machine learning to provide dynamic, multidimensional, and skill-set-specific quality assessments. The framework significantly outperforms current reputation systems. By providing more representative reputation scores, the framework helps workers to differentiate, employers to make informed decisions, and the market to improve its recommendation algorithms and understand the supply distributions across different dimensions. The framework generalizes in other contexts where reputation systems are overly positive and unidimensional. The framework highlights how combining human input with advanced machine learning techniques can augment intelligence by creating the necessary conditions for humans to make informed decisions. Such systems have the potential to increase efficiency and outcome quality precisely because they intelligently differentiate workers. The deployment of the proposed intelligence augmentation framework in different types of online platforms could have implications for workers, employers, businesses, and the future of work.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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