Improvement and Application of Weight-Based Recommendation Algorithms

Although collaborative filtering algorithms are widely used in the field of job recommendation, there is still room for optimization in terms of weight allocation and personalized recommendation accuracy. This paper proposes a collaborative filtering recommendation algorithm based on weight optimization, which significantly improves the recommendation accuracy and efficiency in recruitment scenarios by integrating user attributes, historical behavioral data and multi-dimensional weights (such as region and salary), combined with deep learning technology. The system firstly uses collaborative filtering algorithm to construct user rating matrix, combined with Pearson similarity calculation to achieve preliminary matching; secondly, it designs dynamic weight allocation mechanism to strengthen the impact of key features (e.g., region preference, salary requirement) on the recommendation results; and it further integrates deep neural network (DNN) to mine the deeper features of user behaviors and optimize the recommendation effect of long-tailed data. The system supports recruiters to screen high-match resumes through structured conditions (e.g., keywords, job requirements), and at the same time provides personalized job recommendations for job seekers. The experimental results show that the algorithm is better than the traditional collaborative filtering method in terms of recommendation accuracy and user satisfaction, effectively solves the problem of matching the needs of both sides of the recruitment, and has high practical application value.