Recommendation System for Job Matching
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Terminology
Content-based filtering
Content-based filtering leverages the similarity between items to suggest options that closely align with users' preferences. Unlike collaborative filtering methods, it does not rely on data from other users, making it well-suited for catering to the unique tastes of individual users. By focusing on feature representation based on existing user interests, this model delivers personalized recommendations. However, a limitation of this approach is that it may not effectively broaden users' horizons by introducing them to new, diverse interests.
Collaborative Filtering
Collaborative filtering is a recommendation technique that utilizes similarities between both user queries and items to generate personalized suggestions. By identifying that user A shares similar preferences with user B, the system can confidently recommend user A's choices to user B. This approach has the advantage of accommodating users with limited domain knowledge and facilitating the discovery of new interests. However, to effectively address the cold start problem and ensure accurate recommendations, the system requires the implementation of Weighted Alternating Least Squares (WALS) or similar techniques.
Matrix Factorization
Matrix factorization is a popular technique used in recommendation systems to predict users' preferences for items based on their past interactions. The main idea behind matrix factorization is to decompose the user-item interaction matrix into two or more lower-dimensional matrices, which can then be used to estimate the missing entries in the original matrix.
To train a matrix factorization model, you need to minimize the difference between the estimated ratings and the actual ratings in the user-item interaction matrix. This is typically done by optimizing an objective function, such as the mean squared error (MSE) or the regularized mean squared error (RMSE), using gradient descent or alternative optimization algorithms.
The most common matrix factorization techniques are Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and Alternating Least Squares (ALS). Each of these techniques has its own advantages and trade-offs in terms of computational complexity, scalability, and accuracy.
Recommendation using embeddings and nearest neighbor search
Distance Function
Given that cosine similarity can be calculated more efficiently by utilizing just a dot product, it leads to expedited processing times. As both cosine similarity and Euclidean distance generate identical rankings, cosine similarity emerges as a highly suitable and computationally efficient choice for comparing OpenAI embeddings.
Prototype
The algorithm takes into account four essential parameters: expected salary, education level, certifications, and work experience. Additionally, it considers two optional parameters: skills and awards. Users have the flexibility to customize the ratio and range of these parameters according to their specific preferences and requirements.
Besides, employers can consider candidates’ side projects via URLs, uploaded portfolios, documents, and links to SCM tools. Although it won’t be considered in the algorithm, it will be a reference for employers at this stage.
Extracting Requirements from JD
This prompt instructs the user to extract specific information from a given text related to job requirements or candidate profiles. The user is required to identify education levels, education backgrounds, years of working experience, must-have skills, and nice-to-have skills.
The extracted information should then be formatted as a JSON object with the keys education_level, education_background, year_of_working_experience, must_have_skills, and nice_to_have_skills.
Algorithm
To enhance the assessment of candidates, compute the education score and relevant work experience score by assigning numerical values to their academic backgrounds and years of experience in the field, respectively.
The code above normalizes a set of talent factors stored in a NumPy array called talents_factors. It uses min-max normalization to scale the values between 0 and 1. Then, it creates a KDTree (k-dimensional tree) data structure using the normalized talent factors to efficiently search for the closest neighbors.
The user's input is also normalized using the same min-max normalization technique. The KDTree is queried to find the 10 closest neighbors (talents) to the user's input. The function returns the indices of the closest employees in the talents_factors array. Finally, the code prints the indices of the closest employees found.