Deep Learning-Driven Personalized Recommender Systems: Theory,

Models, and Future Directions

Xingzhe Feng

and Ziyi Sheng

BigData, China University of Geosciences, Wuhan, China

Mathematics and Applied Mathematics, Chongqing University of Arts and Sciences, Chongqing, China

Keywords: Deep Learning, Recommendation System, Algorithm.

Abstract: Deep learning-based recommendation algorithms have emerged as a significant area of interest within

artificial intelligence research. This surge in attention is primarily due to the limitations of conventional

recommendation systems, coupled with the rapid advancements in deep learning technologies. In this work,

we provide an overview of deep learning-based recommender systems, including their key stages and

components. The pipeline will be explicated, specially, data collection and preprocessing, feature engineering,

model selection and training, evaluation and the deployment with online learning mechanisms would be

stressed. Additionally, we introduce a novel deep learning-based recommender system named Stratified

Advance Personalized Recommendation System (SAP Model). This system solves the problem in the

recommendation of the cold start, overspecialization, and data sparsity. By Stratified, we mean that this

method personalizes the recommendation by clustering the users who have similar interactions or

demographics. The architecture, training techniques, and evaluation measures of the SAP Model will also be

covered, which gives us a glance at the improvement of the effectiveness of recommendations and the

satisfaction of users in the real world.

1 INTRODUCTION

In recent years, recommendation algorithms based on

deep learning have become a hotspot for research in

the field of artificial intelligence, mainly since

traditional recommendation algorithms face many

challenges, while deep learning technology is

developing rapidly. Traditional recommendation

algorithms mainly rely on statistical methods,

machine learning, and other methods to predict the

user's interest based on the user's historical behavior,

attributes, and other features, and then generate a

recommendation list. However, with the arrival of the

big data era, the amount of data that recommendation

algorithms need to process is getting larger and larger,

and traditional recommendation algorithms have

encountered bottlenecks in accuracy and efficiency.

The emergence of deep learning technology opens

new ideas for the further development of

recommendation algorithms.

https://orcid.org/0009-0007-3221-9153

https://orcid.org/0009-0008-5684-3740

Deep Learning (DL) is a technique that simulates

the structure and function of the neural network of the

human brain. Simply put, it takes the data through

layers of abstraction and representation, and

ultimately obtains the deep features of the data, thus

enhancing the expressive ability of the model. Deep

learning technology systems can extract deep

information such as user interest preferences and

behavioral patterns from massive data, and through

the powerful feature learning ability of neural

networks, achieve accurate and satisfactory

recommendation results.

Covington et al introduced deep neural networks

for YouTube video recommendations—a seminal

work that makes significant progress on using

complex models for capturing users’ content features.

This work shows the great significance of deep

learning in tackling very large datasets and reaches to

a new technical apex of perfectly mapping huge data

and complicated patterns (Covington, 2016).

Feng, X. and Sheng, Z.

Deep Learning-Driven Personalized Recommender Systems: Theory, Models, and Future Directions.

DOI: 10.5220/0012910700004508

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 105-109

ISBN: 978-989-758-713-9

105

In addition, Cheng et al.'s study on deep learning

and recommender systems also showed that it is

feasible to improve the quality and efficiency of

recommendations on Google Play by combining deep

neural networks with linear models (Cheng, 2016).

This study shows the synergy between memory

capacity and generalization capacity and points to a

better direction for using deep learning to improve

recommender systems.

In addition, Zhang et al (Zhang, 2019) proposed a

hybrid personalized recommendation algorithm for a

model building based on blockchain, which can be

seen as a perfect fusion of blockchain and deep

learning. Their study proved that blockchain

technology not only extends and accelerates the DL-

based model but also ensures the security of the

recommendation system. Moreover, this proposal of

hybrid blockchain models does offer a bright future

direction for building more secure, transparent, and

efficient recommender systems that can be fully

adapted to the changing needs of users as well as to

different aspects of the digital ecosystem. As we

explore the nuances of these studies, one thing

becomes clear: the intersection of deep learning and

recommender systems is not just a fad, but a

transformative tidal wave reshaping the digital

panorama. Each evolution of deep learning-powered

recommender systems heralds our shift to a more

intimate, discreet, and fluid user experience, and

foreshadows the far-reaching role of deep learning in

the next phase of digital consumer products.

Our research aims to investigate deep learning

approaches to personalized recommendations. First,

in this paper, we will improve the performance of

current recommender systems using a combination of

natural language processing and user behavior

analysis, section by section. The next two sections

discuss the details of the related deep learning-based

recommender system approach in Section 2, and the

final section discusses the limitations and future

outlook of this paper. The full paper concludes in

Section 4.

2 METHOD

2.1 Overall Workflow in Deep

Learning-Based Recommendation

Systems

Deep learning-based recommendation systems

usually follow a workflow that involves several main

stages: (1) Data Collection and Preprocessing: In this

stage, data is obtained from various sources like user

interactions, item metadata, and user features such as

profiles, and put in cleaned, normalized and

transformed into forms that is able to train deep

learning models; (2) Feature Engineering: It involves

selecting features, modifying and creating new

features from raw data. In deep learning systems,

feature engineering can also mean learning

representations such as embedding; (3) Model

Selection and Training: In this stage, a deep learning

model architecture is chosen based on the problem.

Popular architectures include Convolutional Neural

Networks (CNN), Recurrent Neural Networks

(RNN), Autoencoder and Transformer models. After

that, the model is trained on preprocessed data. 4)

Evaluation: The performance of the model is

evaluated using metrics such as precision, recall, F1-

score, or mean average precision (MAP). This step

involves tuning the hyperparameters using validation

sets and cross-validation techniques. 5) Deployment

and Online Learning: After the previous training and

evaluation, the model will be deployed to a

production environment to start working in the field.

2.2 Stratified Advanced Personalized

Recommendation System Based on

Deep Learning (SAP Model)

In their paper, Li et al. propose the SAP model, a new

deep learning-based recommender system called

Hierarchical Advanced Personalized Recommender

System, which can solve the problems of cold start,

over-specialization, and data sparsity faced by

traditional systems. The main contribution of this

research is to propose a strategy of hierarchical

personalization, i.e., grouping target users using

human-object interactions and demographic

information and using different deep learning

methods for different groupings, which greatly

improves the quality of recommendations.

Proposal: To accomplish our aims, we provide the

SAP Plan, employing various revolutionary deep

learning forms: 1) CBOW-CNN_FT is a word

embedding tactic that catches connections within

linguistic components conveyed by sematic surface,

and 2) EINMF can make personalized

recommendations using explicit feedback and

implicit feedback separately based on matrix

factorization model. Graph Neural Networks (GNNs)

are used to re-rank items on their scores in the final to

enhance the accuracy of the recommendation. It

applies the softmax layer to predict which items users

are likely to interact with, and k-means clustering to

make personalized user segmentation. With an

intermediate item pool and a product layer that

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integrates the outputs of various deep-learning

components prior to final reordering.

To compute and rank the recommender system,

the model uses a mixture of user item embeddings and

architectural components such as Convolutional

Neural Networks (CNNs) and Graph Neural

Networks (GNNs). In addition, the recommender

system is equipped with advanced NLP techniques to

understand item relationships, as evidenced by the

fact that the model employs techniques such as the

BPR loss function and CBOW-CNN_FT for semantic

analysis. In order to generate the recommendation

list, the model applies a secondary ranking procedure

at the end, which takes into account the item

relationships and user preferences to finally generate

the ideal recommendation list (Yu, 2023).

2.3 Deep Learning Recommendation

Model for Personalization and

Recommendation Systems (DLRM)

Innovative Algorithm: The DLRM is unique in that it

combines embeddings of categorical features with a

multilayer perceptron (MLP) for continuous features.

A key innovation is how the interactions between

features are handled, in particular how the

interactions between crosswords and their embedded

feature vectors are handled. Dot products are used to

reduce dimensionality and highlight meaningful

interactions.

Methodology: The methodology of our model is

an organized interaction embedding approach that

mimics a factorization machine but concentrates on

the crosswords generated by the embedding.

Compared to most networks where higher-order

interactions may be modeled, our proposed approach

greatly reduces the size of the model. In addition, we

have deliberately designed detailed methods to

efficiently handle the huge parameter space,

balancing the model data parallelism of embedding

models with the model parallelism of MLPs, and

these efforts result in an excellent trade-off between

the modeling and acceleration capabilities of our

models.

Implementation: DLRM has been carefully

designed to achieve optimal scalability and efficiency

to break through the practical limitations of training

large-scale complex models. This requires the use of

sophisticated parallelization techniques, including

butterfly shuffling that facilitates personalized

communication between devices, which ensures

robust training and deployment of the model at scale.

The diverse use of synthetic and real datasets in

training and evaluation enhances the flexibility and

potential of the model to be applied to real-world

problems.

2.4 Deep Learning Techniques in

Recommender Systems

Multi-Layer Perceptrons (MLPs): MLPs capture non-

linear interactions between user and item features.

The MLP is a fundamental technique in DL applied

to recommendation that enables the model to learn the

complex user-item relationship.

Convolutional Neural Networks (CNNs): Applied

mainly for content-based recommendations,

especially when items are associated with visual or

textual information. CNNs are effective in extracting

features from images or text, improving

recommendations by utilizing content features.

Recurrent Neural Networks (RNNs): Suitable for

sequential recommendation tasks where the order of

interactions matters, such as predicting the next item

a user might be interested in. RNNs and their variants

(like LSTM and GRU) are adept at modeling time-

dependent data.

Autoencoders: Used for collaborative filtering by

learning compact representations of user or item

profiles. Variants like denoising autoencoders and

variational autoencoders can help in learning robust

features from input data, enhancing recommendation

accuracy.

Attention Mechanisms and Transformers:

Introduced to recommender systems to focus on

relevant parts of the data, improving the model's

ability to capture important interactions.

Transformers, leveraging self-attention, have been

particularly influential in modeling sequential

interactions and contexts (Naumov, 2019).

3 DISCUSSIONS

With the continuous development of artificial

intelligence technology, deep learning has become a

key technology in multiple fields (Lambert, 2024;

Qiu, 2020). However, despite its significant success

in many tasks, deep learning still faces some core

problems and challenges, especially the issues of poor

interpretability and generalization. This paper will

delve into these problems from the perspective of

product and industrial applications, combining

solutions such as interpretability algorithms, transfer

learning, and domain adaptation.

Deep learning models, especially complex neural

networks, often struggle to explain their internal

decision-making processes and output results. This

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107

limits the application of deep learning models in

fields that require interpretability, such as healthcare

and finance. In addition, as model complexity

deepens, model interpretability becomes difficult

(Cheng, 2016). Deep learning is usually trained on

massive amounts of data. However, deep learning

may not perform well when encountering new and

unknown data. This directly affects the model's

ability to generalize in real-world applications. Real-

world data does not fully cover all possible scenarios,

and improving the generalization ability of models is

a crucial task. In addition, as deep learning deepens

the coverage of the product, explainability becomes

important because users want to know why the model

makes a particular decision and expect the product to

provide an intuitive and easy-to-understand

explanation. Therefore, future products will focus

more on user experience and interpretability. We can

provide a visual explanation of the decision basis of a

product using interpretable algorithms such as

SHapley Additive exPlanations (SHAP), which

calculates the contribution of each input feature to the

output of the model, thus allowing the user to

understand the decision process of the model. In

addition, we can develop interactive tools that allow

the user to interact directly with the model so that the

user can have a clearer understanding of the model's

decision-making process. For example, change the

model's decision maker with some evidence to

explore whether there is a problem with the model

output. In the industrial domain, deep learning models

need to be more stable, reliable, and have good

generalization capabilities, and the efficiency and

scalability of the models need to be considered to

cope with large-scale data and complex scenarios.

Learning models for industrial domains are enabled

by transfer learning, where knowledge from the

source domain can be migrated to the target domain,

thus reducing the dependence on data volume and

enhancing the generalization ability of the model.

Domain adaptation can help the model better adapt to

the data distribution of the target domain and improve

the generalization ability of the model, so it is also

necessary to fine-tune and optimize the model for the

specific data and tasks in the target domain.

4 CONCLUSIONS

This paper is a comprehensive review of the field of

Deep Learning combined with Recommender

Systems. The research methodology of this paper

focuses on the algorithmic structure of CNN, RNN,

Auto-Encoders, and Transformers, and

comprehensively analyzes the shortcomings of the

existing learning models including Cold-start, over-

specialization, data sparsity, etc. In short, the current

recommender systems have flaws and limitations in

terms of accuracy and performance and thus require

further research and improvement.

In the future, in order to better improve the ability

to explain the user experience of the product, the main

focus is to further enhance the model's scalability

with reference to migration studies, domain

adaptation, and model centralization techniques. In

addition, developing more efficient algorithms with

higher forward accuracy is also a major direction for

future research.

AUTHORS CONTRIBUTION

All the authors contributed equally, and their names

were listed in alphabetical order.

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