This paper provides an introduction to hyperpersonlized recommender systems. Rem and Rodrigo aim to provide a more in depth discussion of their research on this topic in separate videos and white papers.

Hyperpersonalized recommender systems are a type of recommendation system that aim to provide highly personalized recommendations to users by considering a wide range of factors such as user preferences, demographics, and behavior. A distinction can be made between systems that recommend content to one user, such as NetFlix, and systems that recommend users to users, such as Tinder. In the later case, the preferences of two users must be taken into consideration. These types of system have been widely studied in the literature, and several approaches have been proposed to achieve hyperpersonalization.

One approach is to use collaborative filtering, which is based on the idea that users who have similar preferences in the past will have similar preferences in the future. This approach uses user-item interactions to predict the preferences of a target user for a given item. Collaborative filtering can be further divided into two categories: user-based and item-based. User-based collaborative filtering is based on the similarity between users, while item-based collaborative filtering is based on the similarity between items. Both of these approaches have been shown to be effective in providing personalized recommendations.

Another approach is to use content-based filtering, which is based on the idea that items similar to those a user has liked in the past will be liked in the future. Content-based filtering uses the features of items to determine their similarity, and it is generally based on the idea of vector space models. This approach has been shown to be effective in providing personalized recommendations, especially when used in combination with other approaches like collaborative filtering.

A third approach is to use knowledge-based recommender systems which are based on experts' knowledge and domain knowledge for providing recommendations to users. This approach is based on the idea of providing recommendations based on the user's goals and needs.

A fourth approach is to use deep learning-based recommender systems, which are able to learn complex representations of users and items, and to provide more accurate recommendations. These systems are based on neural networks and have been shown to be effective in providing personalized recommendations. One of the key advantages of deep learning-based recommender systems is their ability to handle large amounts of data and make predictions based on that data. They can also handle missing data and noisy data, which traditional recommender systems struggle with. Additionally, deep learning models are able to learn non-linear relationships between the data, which can result in more accurate recommendations. Another advantage is the ability to handle more complex data, such as images and text, which can be used to make more personalized recommendations. For example, a deep learning model can be trained on images of clothing to recommend clothing items to users based on their past behavior and the features of the clothing items. However, deep learning-based recommender systems also have some limitations. One limitation is that they require a large amount of data to train the model. Additionally, they can be computationally intensive, which can make them difficult to implement in real-world applications.

A fifth approach is to use hybrid recommender systems, which combine the advantages of multiple recommendation techniques. Hybrid recommender systems can be divided into two categories: content-based and collaborative, and content-based and demographic. These systems have been shown to be effective in providing personalized recommendations by combining the strengths of different recommendation techniques.

Recent research has also focused on the use of explainable AI (XAI) in recommender systems. The goal of XAI is to provide users with insights into the reasoning behind the recommendations provided by the system. This can help users to better understand the system and to trust its recommendations.

There have been many recent research papers on deep learning-based recommender systems. Some examples include:

• "Factorization Machines" by Steffen Rendle, published in the Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM '10). This paper introduces the concept of factorization machines, which are a type of hybrid recommender system that combine the strengths of both matrix factorization and linear models to provide personalized recommendations.
• "Context-Aware Recommender Systems" by Gediminas Adomavicius and Alexander Tuzhilin, published in the Proceedings of the 2005 International Conference on Intelligent User Interfaces (IUI '05). This paper discusses the importance of context in recommender systems and proposes a framework for building context-aware recommender systems.
• "Collaborative Filtering with Temporal Dynamics" by Yehuda Koren, published in the Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09). This paper proposes a temporal collaborative filtering algorithm that takes into account the time-evolving preferences of users and items.
• "Deep Neural Networks for YouTube Recommendations" by Paul Covington, Jay Adams, and Emre Sargin, published in the Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). This paper describes the neural network architecture used by YouTube for personalized video recommendations, which combines deep learning with collaborative filtering and other techniques.
Also of interest:
• "Neural Collaborative Filtering" by X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua. This paper proposes a neural collaborative filtering approach that uses a multi-layer perceptron to model user-item interactions.
• "Deep Matrix Factorization Models for Recommender Systems" by R. Sedhain, A. Menon, S. Sanner, and L. Xie. This paper proposes a deep matrix factorization approach that uses neural networks to model user-item interactions.
• "Collaborative Deep Learning for Recommender Systems" by Y. Wang, J. Wang, Y. Ye, and D. Li. This paper proposes a collaborative deep learning approach that uses neural networks to model user-item interactions.
• "Deep Interest Network for Click-Through Rate Prediction" by Guo et al. This paper introduces an interest-based deep neural network model for click-through rate prediction in recommendation systems.
• "AutoRec: Autoencoders Meet Collaborative Filtering" by S. Sedhain, A. Kveton, J. C. Sanner, M. C. Paul and L. Xie. This papers propose an autoencoder based approach for collaborative filtering.

These papers provide a good starting point for understanding the state-of-the-art in deep learning-based recommender systems, but we would recommend looking at the most recent publications and conferences in the field of recommender systems, such as RecSys, WWW and CIKM, to stay updated on the latest research developments.

Hyperpersonalized recommender systems have been widely studied in the literature and several approaches have been proposed to achieve hyperpersonalization. Collaborative filtering, content-based filtering, hybrid recommender systems, deep learning-based recommender systems, and knowledge-based recommender systems have all been shown to be effective in providing personalized recommendations. Additionally, the use of explainable AI in recommender systems is gaining traction as it can help users better understand and trust the recommendations provided by the system.

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