Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

High-content screening is an empirical strategy in drug discovery toidentify substances capable of altering cellular phenotype — the set ofobservable characteristics of a cell — in a desired way. Throughout thepast two decades, high-content screening has gathered significant attentionfrom academia and the pharmaceutical industry. However, imageanalysis remains a considerable hindrance to the widespread applicationof high-content screening. Standard image analysis relies on feature engineeringand suffers from inherent drawbacks such as the dependence onannotated inputs. There is an urging need for reliable and more efficientmethods to cope with increasingly large amounts of data produced. This thesis centres around the design and implementation of a deeplearning-based image analysis pipeline for high-content screening. Theend goal is to identify and cluster hit compounds that significantly alterthe phenotype of a cell. The proposed pipeline replaces feature engineeringwith a k-nearest neighbour-based similarity analysis. In addition, featureextraction using convolutional autoencoders is applied to reduce thenegative effects of noise on hit selection. As a result, the feature engineeringprocess is circumvented. A novel similarity measure is developed tofacilitate similarity analysis. Moreover, we combine deep learning withstatistical modelling to achieve optimal results. Preliminary explorationssuggest that the choice of hyperparameters have a direct impact on neuralnetwork performance. Generalised estimating equation models are usedto predict the most suitable neural network architecture for the input data. Using the proposed pipeline, we analyse an extensive set of images acquiredfrom a series of cell-based assays examining the effect of 282 FDAapproved drugs. The analysis of this data set produces a shortlist of drugsthat can significantly alter a cell’s phenotype, then further identifies fiveclusters of the shortlisted drugs. The clustering results present groups ofexisting drugs that have the potential to be repurposed for new therapeuticuses. Furthermore, our findings align with published studies. Comparedwith other neural networks, the image analysis pipeline proposedin this thesis provides reliable and better results in a shorter time frame.

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Research on Recommendation Systems using Deep Learning Models

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4609.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 10544-10551

Keyword(s):

Deep Learning ◽

Recommender Systems ◽

Recommendation System ◽

Recommendation Systems ◽

Learning Models ◽

Machine Learning Technique ◽

Learning Technique ◽

Future Preference ◽

Movie Recommendation ◽

News Recommendation

Recommender System is the effective tools that are accomplished of recommending the future preference of a set of products to the consumer and to predict the most likelihood items. Today, customers has the ability to purchase or sell different items with advancement of e-commerce website, nevertheless it made complicate to investigate the majority of appropriate items suitable for the interest of the consumer from many items. Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory. In recent days, various recommendation methods are applied to resolve the data abundance setback in numerous application areas like movie recommendation, e-commerce, news recommendation, song recommendation and social media. Even if all the available current recommender systems are successful in generating reasonable predictions, these recommendation system still facing the issues like accuracy, cold-start, sparsity and scalability problem. Deep learning, the recently developed sub domain of machine learning technique is utilized in recommendation systems to enhance the feature of predicted output. Deep Learning is used to generate recommendations and the research challenges specific to recommendation systems when using Deep Learning are also presented. In this research, the basic terminologies, the fundamental concepts of Recommendation engine and a wide-ranging review of deep learning models utilized in Recommender Systems are presented.

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Performance Analysis of Deep Learning Architectures for Recommendation Systems

2018 IEEE 25th International Conference on High Performance Computing Workshops (HiPCW) ◽

10.1109/hipcw.2018.8634192 ◽

2018 ◽

Cited By ~ 1

Author(s):

Devika Anil ◽

Anagha Vembar ◽

Srinidhi Hiriyannaiah ◽

Siddesh G.M. ◽

K.G Srinivasa

Keyword(s):

Deep Learning ◽

Performance Analysis ◽

Recommendation Systems ◽

Learning Architectures

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Influences of Deep Learning on Recommendation Systems

Helix ◽

10.29042/2018-4340-4344 ◽

2018 ◽

Vol 8 (6) ◽

pp. 4340-4344

Author(s):

Neha R. Kasture

Keyword(s):

Deep Learning ◽

Recommendation Systems

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A Novel Hybrid Deep Multi-Criteria Model for Recommender System

10.21203/rs.3.rs-836949/v1 ◽

2021 ◽

Author(s):

Nour Salim Nassar

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recommender Systems ◽

Recommender System ◽

Real World ◽

Deep Neural Network ◽

Recommendation Systems ◽

The Other ◽

Learning Techniques ◽

Novel Model

Abstract Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.

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