With the vast amount of exponential information flow on the Web, the people often meet the information overload problem while accessing various products or services.

The recommender system has become a popular and vital tool in addressing the information overload problem in the unprecedented growth of online information.

For example, Netflix, Amazon, Youtube, and other web services rely on recommender systems that seek to determine the user preferences from the online activities of the customers.

The revolutionary technology of deep learning has transformed the recommendation model from its infancy stage to the matured system to tackle dynamic challenges online users face across the enormous information.

By suggesting the user anticipations, the recommender system paves the way for the users to make the appropriate decisions in different domains.

In the real world, recommender applications involve booking a restaurant or hotel or ticket, watching a movie, purchasing a product, etc.

With the increasing demand for the recommender system, predicting the users’ desired products or services from the available online information, purchasing behaviors, and historical activities of the customers in the corresponding recommendation sites and cross-domain sites has become imperative.

Recommendation engines are broadly categorized into content-based and collaborative filtering-based systems. Several widely emerging recommender systems are presented as follows.

Content-based Filtering:
The content-based recommendation suggests the relevant products or services to the users based on previously searched or purchased content of the products or services by the users. In this type of recommendation, content refers to the features or attributes tagged with the products or services.

Collaborative Filtering:
The collaborative recommendation provides the products or services based on the user-item interactions. User-based and item-based collaborative filtering are two types of collaborative filtering models. From the knowledge of other similar users’ preferences and past interactions between the users and items, the user-based collaborative filtering model recommends the new items to the users. Item-based collaborative filtering recommends the new items to the users based on the interests of the users in the neighboring items.

Demographic-based Recommender System:
Demographic information-based recommendation mitigates the need for the historical user ratings on the items in the content and collaborative-based filtering. With the knowledge of the users’ attributes in the demographic classes, the demographic-based approach suggests the desired items to the users.

Utility-based Recommender System:
Utility-based recommendation examines the utility of the items for an individual user during the suggestion of the items. With the influence of the non-product attributes of the item availability and vendor reliability, the utility-based recommender system provides the highly preferable and reliable items to the users.

Session-based Recommender System:
The session-based recommender system contemplates the user interactions within a session to recommend the desired items to the users and supports the recommendation when there is a lack of available historical behaviors. Travel, music, video, and e-commerce are the popular domains of the session-based recommendations.

Cross-domain Recommender System:
Cross-domain recommendation models handle the data sparsity constraint by leveraging richer information from the multiple domains. The cross-domain recommendation model has become popular in real-world applications to suggest new items or provide recommendations for new users.

Mobile Recommender System:
The mobile recommender system provides context-sensitive and personalized recommendations based on internet-based smartphones. With the influence of the spatial and temporal factors, the mobile recommendation model suggests the relevant items or services to the users.

Conversational Recommender System:
With the target of improving the success rate of the conversation on the e-commerce sites, a conversational recommender system has emerged. The integration of the recommender systems and the dialog or natural language processing systems relies on the rating history or purchasing behavior of the users to fulfill their information needs of the users. For example, eBay, Alibaba, Google, and Amazon companies satisfy their customers and improve their business opportunities through conversational chatbots.

Federated Recommender System:
Federated Recommender System provides the relevant recommendations to the users based on the interactions of the users on the distributed resources. The Federated recommendation model ensures the data security and privacy by decentralizing the users’ private data locally and enforces the decentralized recommendation with the instantiation of the federated learning.

Industry practitioners and researchers encounter some of the prominent challenges in the recommender system, which are discussed as follows.

Data Sparsity:
The recommendation model often deals with the sparsity constraint because most users are reluctant to submit the ratings or reviews for the purchased items. Data sparsity problem leads to a lack of possibilities in identifying similar interested users.

Cold-start:
In the recommender system, the cold-start problem arises when there is the need to recommend the desired items to the new users and suggest the new items to the users. Lack of availability in the ratings or reviews for the new users and new items complicates the prediction of users’ interests and results in less accurate recommendations.

Search Engine Adaptation:
With the huge data accumulation of the text, audio, and video on the Internet, the online users receive numerous web pages for each search in the search engine. Hence, filtering the relevant search results from the search engine becomes a time-consuming and very tedious task for each user.

Synonymy:
The recommender system confronts the recognition of the interests of the users on the items when a single item is represented with different names, termed synonymy. Synonymy problem creates difficulty for the recommendation models and misleads to the suggestion of relevant items.

Scalability:
In the real world, a huge generation of user interactions creates the scalability issue for the recommendation models, which becomes a major concern in efficiently extracting the users’ preferences from the large-scale data for the recommender system.

Dynamicity:
The recommendation model meets the inaccurate or irrelevant recommendation of the items due to the users’ preferences and behaviors changing over time in the recommendation sites. The user dynamics and item dynamics also greatly affect the recommendation quality due to the lack of time awareness during the generation of the recommendation list.

Privacy:
   With access to personal information such as past purchasing behaviors and ratings or reviews by the recommendation system, customers hesitate to feed their personal information due to privacy and security constraints. Hence, the recommendation models have the obstacle in providing personalized recommendation services, requiring trust-ensured recommendation systems.

Deep learning models play a significant role in ensuring a better customer experience through the development of recommender systems. Several companies have adopted deep learning systems to improve their profit by investigating multiple data abstractions and representation levels. For example, Twitter, eBay, Yahoo, and YouTube adopt the Deep Neural Networks (DNNs), and Spotify adopts the Convolutional Neural Networks (CNNs) for the recommendation. The different neural networks-modeled hybrid approach also improves the satisfaction of the customer experience. Several widely adopted deep learning models in the recommender system are listed as follows.

Multilayer Perceptron:
Feedforward neural networks such as Artificial Neural Network (ANN) feed the data from one layer to the next layer in only a forward manner. Multilayer Perceptron (MLP) is one of the feedforward ANN models that is flexible in implementing the recommendation problem.

Autoencoder:
Autoencoder plays an essential role in the recommendation process, representing and reconstructing the input data in the output layer through its hidden layers. In the recommender system, the autoencoder can handle the processes of dimensionality reduction, feature extraction, and data reconstruction.

Restricted Boltzmann Machine:
Restricted Boltzmann Machine (RBM) ensures high-quality personalized recommendations with the support of scalability for large datasets. A two-layer network of RBM consists of only a visible layer and hidden layer, easily stacked to the deep networks, and inherently investigates the large-scale user interactions without the intra-layer communication in the visible layer or hidden layer.

Recurrent Neural Network:
With the advantage of processing the sequenced data and language patterns, a Recurrent Neural Network (RNN) is capable of investigating the sequential user patterns and temporal interactions. For instance, YouTube suggests a video for a particular time of the day to a particular user. Hence, RNN variants of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models greatly assist such recommendations over temporal dynamics and leverage the design of the session-based recommender system.

Convolutional Neural Network:
CNN is a pertinent deep learning model for processing unstructured multimedia data like text, images, audio, and video. It plays an imperative role in empowering the traditional recommender systems and addressing the cold-start problem due to most of the customers’ decisions on the e-commerce site are based on the visual assessment of the products.

Adversarial Network:
A generative neural network of adversarial networks aids the recommendation relevant to the customers’ decisions based on the simultaneous training of the generator and discriminator.

Deep Reinforcement Learning:
Integrating reinforcement learning with deep neural networks produces human-level actions across multiple domains. With the knowledge of getting the users’ interactions through the agents, deep reinforcement learning suggests the relevant recommendations based on the trial-and-error paradigm.

Neural Attention:
In a neural network, the attention mechanism leverages the prediction of the next item based on the vector of importance weights and filters outs the irrelevant content. As the inspiration of the attention mechanism in the domains of natural language processing and computer vision through the correlation with others, like the next work in a textual sentence or a pixel in the image, neural attention assists the recommender system in producing the relevant recommendations. The neural attention model is widely integrated with the DNN or CNN models to select the most representative items in the recommendation system.

Transformer Deep Learning:
Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based deep learning model that parses a sentence based on attention to relevant words only and is an alternative model to RNN variants. Transformer deep learning model enables the parallelization regardless of the sequential data processing and hence, reduces the training time than the RNN models, applicable to the preference modeling on the recommender systems.

Federated Learning for Recommender Systems
   • To ensure the trust among their customers by providing the personalized recommendations without acquiring the personal information of the users.

Conversational Recommender System
   • To improve the quality of the voice-controlled home assistants and chatbot technology in the area of natural language processing.
   • To ensure a richer set of interactions based on the user preferences and create the business opportunities by enabling the users to provide the feedback and ask the questions about the recommendations.

Aspect based Opinion Mining for Personalized Recommendation
   • To design the personalized recommendation model from the inherent analysis of the users’ reviews on the items.
   • To extract the opinion of the users of customers on the items for the aspect analysis of the expressed emotions.

Personalized Recommendation with Contextual Pre-Filtering
   • To personalize the recommendation with the contextual information of the users and items.
   • To filter the preferences of the users depending on the ratings or reviews submitted by the users in a dynamic context before the generation of the personalized services.

Temporal and Spatial Context-based Group Recommendation
   • To build the group recommendation model from both the temporal and spatial context of the users’ preferences on the items.
   • To extract the insights of an individual and the group of the users on the items within a particular time interval and at a particular location.

Session-based Recommender System with Representation learning
   • To contemplate the session context of the user ratings, reviews, or purchasing behaviors during the generation of a list of user preferences.
   • To extract the session-based user preferences through representation learning to suggest the relevant items across dynamic behavior changes of the users.

Serendipity-aware Product Recommendation
   • To improve the user satisfaction in the product recommendation with the target of ensuring serendipitous products from the unforeseen explicit preferences.
   • To recommend the unanticipated and desired product to the users based on the inherent preference analysis from their numerous activities.

Deep Preference Prediction for Novelty and Diversity-Aware Top-N Recommendation
   • To suggest the top-N recommendation with the novel items that are not previously purchased and desired by the users.
   • To predict the user preferences and recommend the novel and relevant items with coverage of highly preferred item categories in the top-N list.

Personalized Recommendation with Neural Attention Model
   • To build the recommendation model with the neural attention mechanism to improve the extraction of user preferences.
   • To personalize the recommendation based on the self-attention in the neural network model.

Contextual Recommendation with Deep Reinforcement Learning
   • To design the contextual recommendation model regardless of the historical training knowledge of the user preferences on the items.
   • To adopt the deep reinforcement learning model for the adaptive recommendation of the contextual user preferences.

Deep Neural Network-based Cross-Domain Recommendation
   • To utilize the users’ preferences from the user-involved multiple domains to ensure the recommendation quality.
   • To develop the cross-domain recommendation model with the improved deep neural network model.

Sparsity Handling in Recommender System with Transfer Learning
   • To address the data sparsity to improve the recommendation quality through the transfer learning model.
   • To enhance the user preferences with the knowledge transferred from the source domain to provide the relevant items to the users.

Deep Learning-based Domain Adaptation for Recommendation
   • To adopt the deep learning model to predict user preferences on the previously known and unknown items.
   • To adapt the domain knowledge during the preference extraction to improve the recommendation performance and user satisfaction.

Topic Modeling for Personalized Product Recommendation
   • To determine the topic of user preferences, recommend the preferred topic-based items.
   • To provide the personalized recommendation through topic modeling-based user preference extraction.

Social Information based People Recommendation
   • To examine the social information of the multiple users to ensure the desired recommendation of people.
   • To suggest the people in the social network based on the similar patterns or behaviors of the social users.

Deep Learning-based Explainable Recommender Systems
   • To recommend the personalized services to the users with the explainable motive behind the recommendations.
   • To build the explainable recommendation model with the deep learning model that assists the extraction of instinctive user preferences.

Explicit and Implicit Feedback-Aware Top-N Recommendation
   • To extract the user preferences from the analysis of both the implicit and explicit feedback of the users.
   • To generate the top-N recommendation with increased user satisfaction and recommendation quality.

Hybrid Deep Learning-based Recommender Systems
   • To integrate the deep learning models to generate the desired set of items for the users.
   • To build the personalized recommendation model with the deep hybrid learning-based feature extraction and preference prediction.