Chain of thoughts Notes
Q.
expand on this algorithm. how can i build it?
- Core Algorithm: Hybrid Recommendation System This is both a search (matching researcher/grant embeddings) and recommendation (personalized ranking with RL) system.
Key Components A. Content-Based Filtering (Initial Layer)
Researcher Embeddings:
Generate from OpenAlex data (publications, keywords, institutions) using NLP models (e.g., all-mpnet-base-v2 from Sentence Transformers).
Precompute for 2000 Texas researchers; fetch new users in real-time via OpenAlex API.
Grant Embeddings:
Encode grant text (title, description, eligibility) with the same NLP model.
Similarity Matching: Use cosine similarity between researcher/grant embeddings.
B. Success Factor Scoring (Secondary Layer)
Combine similarity scores with grant-specific metrics:
Historical success rate (e.g., acceptance rate for the grant).
Funding amount/deadline proximity.
Alignment with the researcher’s institution/department.
Example formula: Final Score = (0.7 × Similarity) + (0.3 × Success Factor)
C. Reinforcement Learning (Feedback Loop)
Model: Use contextual bandits (a lightweight RL approach) to learn from user likes/dislikes.
Input Context: Researcher embedding + grant metadata.
Action: Recommend a grant.
Reward: +1 for a "like," -1 for a "dislike."
Update the model incrementally using feedback.
CoT: Okay, let's try to figure out how to build this hybrid recommendation system. The user provided a good outline with content-based filtering, success factors, and reinforcement learning. But I need to expand on each part and figure out the steps to actually build it. Let me start by breaking down each component and think through the details.
First, the content-based filtering layer. They mentioned using OpenAlex data for researchers and grants. For researchers, they precompute embeddings for 2000 Texas researchers using all-mpnet-base-v2 model. New users are fetched in real-time. So, I need to get data from OpenAlex. How do I access their API? Maybe check their documentation for endpoints. For researchers, publications, keywords, institutions are important. Need to concatenate all that text into a single string or process them separately? Maybe combine titles, abstracts, keywords into a single text block for each researcher. Then use the Sentence Transformers model to generate embeddings. Similarly for grants, take title, description, eligibility criteria and encode them.
The similarity matching uses cosine similarity. So, after generating embeddings, store them in a vector database? Like FAISS or Annoy for efficient similarity search. But since they have 2000 researchers, maybe it's manageable without a complex DB, but scalability might be an issue later.
Then the success factor scoring. This combines similarity with other metrics. Historical success rate, funding amount, deadline proximity, and alignment with institution. How to get historical success rate? Maybe if the system has data on past applications and their outcomes. But if starting fresh, this might be a problem. Maybe start with default values or use external data sources. Funding amount and deadline proximity are easier—grant data should include those. For alignment with institution, maybe check if the grant is restricted to certain institutions or if there's a history of the researcher's institution applying.
The formula is 0.7 similarity + 0.3 success factors. But how to normalize the different success factors? Each factor might be on a different scale. Need to normalize them to a 0-1 scale before combining. Also, how to weight each sub-factor within the 0.3? Maybe assign weights to each of the success metrics. For example, historical success rate (0.5), funding amount (0.2), deadline (0.2), alignment (0.1). Then sum those and multiply by 0.3. Need to decide on those weights.
Then the RL part with contextual bandits. Contextual bandits are a type of RL where the model chooses an action (recommend a grant) based on context (researcher and grant info) and gets a reward. The model needs to update based on feedback. So, when a user likes or dislikes a recommendation, that's a reward signal. The challenge is how to update the model incrementally. Maybe use a library like Vowpal Wabbit which supports contextual bandits. The input context would be the concatenated researcher embedding and grant metadata. The action space is the list of possible grants, but with a large number, this could be tricky. Maybe pre-filter grants using the content-based layer and then use the RL model to rank the top N.
Implementation steps. First, data collection. For researchers, pull data from OpenAlex API. Need to handle authentication, rate limits, and data parsing. For grants, maybe another database or API. Then preprocessing: cleaning text, handling missing data, normalizing fields like funding amount.
Embedding generation. Use Sentence Transformers to encode researcher and grant texts. For grants, maybe combine title, description, eligibility into one text. Then compute similarity matrix between researchers and grants. But with 2000 researchers and, say, 1000 grants, that's 2 million similarities. Storing that might be memory-intensive. Maybe compute on the fly when a researcher logs in, using approximate nearest neighbors for speed.
Success factors. For each grant, calculate the historical success rate. If no data, perhaps assume a default value. Funding amount could be normalized by the max funding in the dataset. Deadline proximity could be days until deadline, inverted and normalized. Alignment with institution: check if the grant's allowed institutions include the researcher's, or if there's a keyword match. Each of these factors needs to be scaled between 0 and 1.
Combining the scores. Apply the formula, then rank grants by the final score. The top N grants are recommended.
For the RL part, when a user provides feedback, record the context (researcher embedding, grant metadata), action (grant recommended), and reward. Then update the contextual bandit model. The model could be a linear model that predicts the expected reward for a given context-action pair. The update step would adjust the model's weights based on the reward. But how to handle the large action space? Maybe after the content-based and success scoring, we have a shortlist of grants, and the RL model re-ranks them. Or the RL model selects from the top 100 grants filtered by the initial layers.
Deployment considerations. The system needs to handle real-time recommendations. Precompute researcher embeddings, but new users require real-time processing. Grants might be added or updated, so their embeddings need to be updated periodically. The RL model needs to be updated incrementally, which might require a database to store feedback and a process to retrain the model periodically or online.
Evaluation metrics. For the recommendation system, use precision@k, recall@k, or NDCG. For the RL part, track the cumulative reward over time. Also, A/B testing between the hybrid system and just content-based or success factor alone.
Potential challenges: Cold start for new grants or researchers with no data. Bias in the RL model if certain grants are recommended more just because they're popular. Scalability when the number of researchers and grants grows. Privacy concerns with handling researcher data.
Tools and libraries: Sentence Transformers for embeddings, FAISS for similarity search, scikit-learn for normalization, Vowpal Wabbit for contextual bandits, Flask/Django for API, PostgreSQL for storing data, and maybe Redis for caching embeddings.
Testing phases: Start with a small dataset, validate each component separately. Test the content-based filtering's accuracy by checking if similar researchers get similar grants. Test the success factors by comparing with expert opinions. Simulate user feedback for the RL model to see if it adapts correctly.
Documentation and monitoring are important. Logging user interactions, model performance, and system latency. Also, user interface considerations: how to collect likes/dislikes, display recommendations, etc.
In summary, building this system involves setting up data pipelines, embedding generation, similarity computation, scoring with success factors, implementing the RL feedback loop, and deploying it all in a scalable and maintainable architecture. Each component has its challenges, especially integrating real-time data and incremental model updates. Need to plan for evaluation, monitoring, and handling edge cases like cold starts.
Q.
Ok i have these data already in my database would this work. i need to find a relevent data source for grants data or should i train with old data and then think about getting a fresh grants data later on? which would be most practical if I am thinking of scale.
the grants data might have a few missing details and I need to work to clean that.
And this is the one for researchers.
Database Tables researchers Filter columns
New column Name Description Data Type Format id
No description
integer int4 researcher_id
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text text researcher_name
No description
text text total_citations
No description
integer int4 total_works
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integer int4 h_index
No description
integer int4 i10_index
No description
integer int4 concept_1
No description
text text concept_1_score
No description
double precision float8 concept_2
No description
text text concept_2_score
No description
double precision float8 years_active
No description
integer int4 recent_works_count
No description
integer int4 recent_citations
No description
integer int4 unique_venues
No description
integer int4 avg_coauthors
No description
double precision float8 open_access_ratio
No description
double precision float8 top_work_1_id
No description
text text top_work_1_type
No description
text text top_work_1_is_oa
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boolean bool top_work_1_keywords
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text text top_work_1_source
No description
text text top_work_2_id
No description
text text top_work_2_type
No description
text text top_work_2_is_oa
No description
boolean bool top_work_2_keywords
No description
text text top_work_2_source
No description