What are: Vectors, Keywords, Parameters, etc.

Question

Hi all;

This is a follow-up to the question Creating a Recommendation Engine.

I asked, and Grace answered:

Q1: I think I need to create vectors of every user and every event. Both have a fair number of boolean and numeric properties (that's straightforward). And for the text properties I need to create an embedding for each - correct? -Vectors are useful for semantic similarities and considered per field, not per entity, since some you would like to keep as keywords only. Good candidates for vectors: descriptions, options that require synonyms, etc. Good candidates for keyword: names, product IDs, IDs in general, org names, etc.

1. Can you please provide more detail about what a keyword is and how it is used when searching for a nearest neighbor? Or if it's not part of the nearest neighbor search, how it is used?
2. And can you provide more detail in determining when to set a data property as a vector vs. when to set it as a keyword?
3. Also, are vectors and keywords both parameters? Or are parameters a 3rd category of data types?
4. Are there any other data property types/categories?

thanks - dave

Answer 1

@David Thielen In the context of AI and machine learning, particularly in recommendation systems, vectors, keywords, and parameters play distinct roles:

• Vectors: These are numerical representations of data that can capture the magnitude and direction of the data points in a multi-dimensional space. In recommendation engines, vectors are often used to represent the features of items or users. For example, user preferences or item characteristics can be encoded as vectors, which can then be compared for similarity using vector operations like cosine similarity or Euclidean distance. Vectors are particularly useful for capturing semantic similarities, especially when derived from text embeddings, which convert text data into numerical form that reflects the meaning of the words.
• Keywords: Keywords are specific terms or identifiers used to index and retrieve data. In search engines, keywords are matched against an index to find relevant documents or items. Keywords are typically used for exact or fuzzy matches and are good for searching specific terms like names, product IDs, or organizational names. They are not part of the nearest neighbor search but are used in traditional search engines to quickly locate documents containing certain words.
• Parameters: In the context of AI models, parameters are the variables that the model learns during training. They define the behavior of the model and are adjusted through the learning process to minimize the error in predictions. Parameters can also refer to the settings or configurations used in algorithms, such as the number of neighbors in a k-nearest neighbors' algorithm or the learning rate in neural networks.

When deciding whether to use a vector or a keyword for a data property, consider the following:

• Use vectors for properties where semantic similarity is important, such as descriptions or features that benefit from understanding synonyms or related concepts.
• Use keywords for properties that require exact or near-exact matches, such as unique identifiers or names where the exact term is important.

Both vectors and keywords can be considered as types of parameters in the broader sense, as they are inputs to the system that affect how searches and recommendations are performed. However, in a more technical sense, parameters often refer to the learned weights in a machine learning model.

Other data property types/categories you might encounter include:

• Boolean: True/False values, often used for binary features.
• Categorical: Discrete values representing categories or groups.
• Continuous: Numeric values that can take any value within a range, often used for measurements or counts.

Each of these types can be represented as vectors for use in machine learning models, depending on the nature of the data and the requirements of the algorithm. In the context of AI and machine learning, particularly in recommendation systems, vectors, keywords, and parameters play distinct roles:

• Vectors: These are numerical representations of data that can capture the magnitude and direction of the data points in a multi-dimensional space. In recommendation engines, vectors are often used to represent the features of items or users. For example, user preferences or item characteristics can be encoded as vectors, which can then be compared for similarity using vector operations like cosine similarity or Euclidean distance. Vectors are particularly useful for capturing semantic similarities, especially when derived from text embeddings, which convert text data into numerical form that reflects the meaning of the words.
• Keywords: Keywords are specific terms or identifiers used to index and retrieve data. In search engines, keywords are matched against an index to find relevant documents or items. Keywords are typically used for exact or fuzzy matches and are good for searching specific terms like names, product IDs, or organizational names. They are not part of the nearest neighbor search but are used in traditional search engines to quickly locate documents containing certain words.
• Parameters: In the context of AI models, parameters are the variables that the model learns during training. They define the behavior of the model and are adjusted through the learning process to minimize the error in predictions. Parameters can also refer to the settings or configurations used in algorithms, such as the number of neighbors in a k-nearest neighbors algorithm or the learning rate in neural networks.

When deciding whether to use a vector or a keyword for a data property, consider the following:

• Use vectors for properties where semantic similarity is important, such as descriptions or features that benefit from understanding synonyms or related concepts.
• Use keywords for properties that require exact or near-exact matches, such as unique identifiers or names where the exact term is important.

Both vectors and keywords can be considered as types of parameters in the broader sense, as they are inputs to the system that affect how searches and recommendations are performed. However, in a more technical sense, parameters often refer to the learned weights in a machine learning model.

Other data property types/categories you might encounter include:

• Boolean: True/False values, often used for binary features.
• Categorical: Discrete values representing categories or groups.
• Continuous: Numeric values that can take any value within a range, often used for measurements or counts.

Each of these types can be represented as vectors for use in machine learning models, depending on the nature of the data and the requirements of the algorithm.