Editing Recommender system (section)

== Artificial intelligence applications in recommendation ==
{{More citations needed section|date=October 2023}}
[[Artificial intelligence]] (AI) applications in recommendation systems are the advanced methodologies that leverage AI technologies, to enhance the performance recommendation engines. The AI-based recommender can analyze complex data sets, learning from user behavior, preferences, and interactions to generate highly accurate and personalized content or product suggestions.<ref>{{cite book |last1=Verma |first1=P. |last2=Sharma |first2=S. |title=2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) |chapter=Artificial Intelligence based Recommendation System |date=2020 |pages=669–673 |doi=10.1109/ICACCCN51052.2020.9362962|isbn=978-1-7281-8337-4 |s2cid=232150789 }}</ref> The integration of AI in recommendation systems has marked a significant evolution from traditional recommendation methods. Traditional methods often relied on inflexible algorithms that could suggest items based on general user trends or apparent similarities in content. In comparison, AI-powered systems have the capability to detect patterns and subtle distinctions that may be overlooked by traditional methods.<ref>{{cite journal |last1=Khanal |first1=S.S. |title=A systematic review: machine learning based recommendation systems for e-learning. |journal=Educ Inf Technol |date=July 2020 |volume=25 |issue=4 |pages=2635–2664 |doi=10.1007/s10639-019-10063-9|s2cid=254475908 }}</ref> These systems can adapt to specific individual preferences, thereby offering recommendations that are more aligned with individual user needs. This approach marks a shift towards more personalized, user-centric suggestions.

Recommendation systems widely adopt AI techniques such as [[machine learning]], [[deep learning]], and [[natural language processing]].<ref name="Artificial intelligence in recommen">{{cite journal |last1=Zhang |first1=Q. |title=Artificial intelligence in recommender systems |journal=Complex and Intelligent Systems |date=February 2021 |volume=7 |pages=439–457|doi=10.1007/s40747-020-00212-w |doi-access=free }}</ref> These advanced methods enhance system capabilities to predict user preferences and deliver personalized content more accurately. Each technique contributes uniquely. The following sections will introduce specific AI models utilized by a recommendation system by illustrating their theories and functionalities.{{Citation needed|date=October 2023}}

=== KNN-based collaborative filters ===
[[Collaborative filtering]] (CF) is one of the most commonly used recommendation system algorithms. It generates personalized suggestions for users based on explicit or implicit behavioral patterns to form predictions.<ref>{{cite journal |last1=Wu |first1=L. |title=A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation |journal=IEEE Transactions on Knowledge and Data Engineering |date=May 2023 |volume=35 |issue=5 |pages=4425–4445 |doi=10.1109/TKDE.2022.3145690|arxiv=2104.13030 }}</ref> Specifically, it relies on external feedback such as star ratings, purchasing history and so on to make judgments. CF make predictions about users' preference based on similarity measurements. Essentially, the underlying theory is: "if user A is similar to user B, and if A likes item C, then it is likely that B also likes item C."

There are many models available for collaborative filtering. For AI-applied collaborative filtering, a common model is called [[K-nearest neighbors algorithm|K-nearest neighbors]]. The ideas are as follows:
# '''Data Representation''': Create a n-dimensional space where each axis represents a user's trait (ratings, purchases, etc.). Represent the user as a point in that space.
# '''Statistical Distance''': 'Distance' measures how far apart users are in this space. See [[statistical distance]] for computational details
# '''Identifying Neighbors''': Based on the computed distances, find k nearest neighbors of the user to which we want to make recommendations
# '''Forming Predictive Recommendations''': The system will analyze the similar preference of the k neighbors. The system will make recommendations based on that similarity

=== Neural networks ===
An [[artificial neural network]] (ANN), is a deep learning model structure which aims to mimic a human brain. They comprise a series of neurons, each responsible for receiving and processing information transmitted from other interconnected neurons.<ref>{{cite journal |last1=Samek |first1=W. |title=Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications |journal=Proceedings of the IEEE |date=March 2021 |volume=109 |issue=3 |pages=247–278 |doi=10.1109/JPROC.2021.3060483|doi-access=free |arxiv=2003.07631 }}</ref> Similar to a human brain, these neurons will change activation state based on incoming signals (training input and backpropagated output), allowing the system to adjust activation weights during the network learning phase. ANN is usually designed to be a [[Black box|black-box]] model. Unlike regular machine learning where the underlying theoretical components are formal and rigid, the collaborative effects of neurons are not entirely clear, but modern experiments has shown the predictive power of ANN.

ANN is widely used in recommendation systems for its power to utilize various data. Other than feedback data, ANN can incorporate non-feedback data which are too intricate for collaborative filtering to learn, and the unique structure allows ANN to identify extra signal from non-feedback data to boost user experience.<ref name="Artificial intelligence in recommen"/> Following are some examples:
* '''Time and Seasonality''': what specify time and date or a season that a user interacts with the platform
* '''User Navigation Patterns''': sequence of pages visited, time spent on different parts of a website, mouse movement, etc.
* '''External Social Trends''': information from outer social media

==== Two-Tower Model ====

The Two-Tower model is a neural architecture<ref>Yi, X., Hong, L., Zhong, E., Tewari, A., & Dhillon, I. S. (2019). "A scalable two-tower model for estimating user interest in recommendations." ''Proceedings of the 13th ACM Conference on Recommender Systems''.</ref> commonly employed in large-scale recommendation systems, particularly for candidate retrieval tasks.<ref>Google Cloud Blog. \"Scaling Deep Retrieval with Two-Tower Models.\" Published November 30, 2022. [https://cloud.google.com/blog/products/ai-machine-learning/scaling-deep-retrieval-tensorflow-two-towers-architecture Accessed December 2024].</ref> It consists of two neural networks:

* '''User Tower''': Encodes user-specific features, such as interaction history or demographic data.
* '''Item Tower''': Encodes item-specific features, such as [[metadata]] or content embeddings.

The outputs of the two towers are fixed-length embeddings that represent users and items in a shared vector space. A similarity metric, such as [[dot product]] or [[cosine similarity]], is used to measure relevance between a user and an item.

This model is highly efficient for large datasets as embeddings can be pre-computed for items, allowing rapid retrieval during inference. It is often used in conjunction with ranking models for end-to-end recommendation pipelines.

=== Natural language processing ===
Natural language processing is a series of AI algorithms to make natural human language accessible and analyzable to a machine.<ref>{{cite book |last1=Eisenstein |first1=J. |title=Introduction to natural language processing |date=October 2019 |publisher=MIT press |isbn=9780262042840}}</ref> It is a fairly modern technique inspired by the growing amount of textual information. For application in recommendation system, a common case is the Amazon customer review. Amazon will analyze the feedbacks comments from each customer and report relevant data to other customers for reference. The recent years have witnessed the development of various text analysis models, including [[latent semantic analysis]] (LSA), [[singular value decomposition]] (SVD), [[latent Dirichlet allocation]] (LDA), etc. Their uses have consistently aimed to provide customers with more precise and tailored recommendations.