User:Bosi Hou/Recommendation system

Introduction
A Recommendation system (RS), or a recommender system is a technique designed to recommend items that a specific user would probably find appealing or relevant [1]. Based on past activities and information of users, it provides customized support by anticipating their present preferences for specific products [2]. It is widely used by E-commerce, streaming services, social media platform, etc., and it has greatly enhanced and shaped the behaviors of consumers in modern time.

Artificial Intelligence (AI) Applications in Recommendation Systems refer to 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. 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. AI-empowered systems, however, are capable of identifying hidden patterns and nuances. They are able to tailor individual needs, and they can provide personalized and satisfying recommendations to the next level.

Recommendation systems widely adopt AI techniques such as machine learning, deep learning, and natural language processing. 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.

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 [3]. 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. The ideas are as follows:
 * 1) 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.
 * 2) Statistical Distance:'Distance' measures how far apart users are in this space. See statistical distance for computational details
 * 3) Identifying Neighbors: Based on the computed distances, find k nearest neighbors of the user to which we want to make recommendations
 * 4) 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
A Neural Network, or 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. 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” 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. 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

Natural Language Processing
Natural Language Processing is a series of AI algorithms to make natural human language accessible and analyzable to a machine [4]. 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.