Build a resource recommendation engine

To build a resource recommendation engine, follow these steps, focusing on either a content-based, collaborative, or hybrid approach, depending on your goals and data availability. Below is a structured plan along with code examples in Python using pandas, scikit-learn, and optionally TensorFlow for deep models.

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Step 1: Define the Use Case

Examples:

• Recommending educational resources based on user interest.

• Suggesting articles or tools based on user behavior or content tags.

• Matching users with learning paths or courses.

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Step 2: Collect and Prepare Data

You need:

• A dataset of resources (title, description, tags, categories, ratings).

• User interaction data (views, likes, time spent, ratings).

Sample resource dataset structure:

csv
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resource_id,title,description,tags,categories,average_rating
1,Intro to Python,"Learn basics of Python",["python", "programming"],["programming", "python"],4.5
2,Data Science 101,"Intro to Data Science",["data science", "statistics"],["data", "statistics"],4.2
...

User interaction dataset:

csv
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user_id,resource_id,interaction_type,timestamp
101,1,view,2024-01-01
101,2,like,2024-01-02
102,1,like,2024-01-05
...

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Step 3: Choose Recommendation Strategy

A. Content-Based Filtering (using TF-IDF)

python
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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Load dataset
resources = pd.read_csv("resources.csv")

# Combine text features
resources["combined"] = resources["title"] + " " + resources["description"] + " " + resources["tags"].apply(lambda x: ' '.join(eval(x)))

# TF-IDF Vectorization
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(resources["combined"])

# Cosine similarity matrix
cos_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)

# Recommend similar resources
def recommend(resource_id, top_n=5):
    index = resources[resources['resource_id'] == resource_id].index[0]
    similarity_scores = list(enumerate(cos_sim[index]))
    similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)[1:top_n+1]
    similar_resources = [resources.iloc[i[0]] for i in similarity_scores]
    return pd.DataFrame(similar_resources)

print(recommend(1))

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B. Collaborative Filtering (User-based or Item-based)

Use Surprise library:

python
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from surprise import Dataset, Reader, KNNBasic
from surprise.model_selection import train_test_split
from surprise import accuracy

# Create reader and load dataset
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(user_data[['user_id', 'resource_id', 'rating']], reader)

# Train/test split
trainset, testset = train_test_split(data, test_size=0.2)

# User-based CF
algo = KNNBasic(sim_options={'user_based': True})
algo.fit(trainset)

# Predict and evaluate
predictions = algo.test(testset)
print("RMSE:", accuracy.rmse(predictions))

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C. Hybrid Recommendation (Combining Content and Collaborative)

You can ensemble predictions from both models:

python
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def hybrid_score(content_score, collaborative_score, alpha=0.7):
    return alpha * content_score + (1 - alpha) * collaborative_score

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Step 4: Add Personalization Layer

Track user preferences using:

• Implicit feedback (clicks, views, duration)

• Explicit feedback (ratings, likes)

Then, match resources that align with the user’s profile using clustering (e.g., k-means) or deep learning (e.g., embedding similarity with TensorFlow or PyTorch).

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Step 5: Build the Interface/API

Use FastAPI for the backend:

python
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from fastapi import FastAPI
app = FastAPI()

@app.get("/recommend/{resource_id}")
def recommend_resource(resource_id: int):
    recommendations = recommend(resource_id)
    return recommendations.to_dict(orient="records")

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Step 6: Evaluate the System

Use metrics like:

• Precision@k, Recall@k

• MAP (Mean Average Precision)

• NDCG (Normalized Discounted Cumulative Gain)

• A/B testing if deployed

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Optional: Deep Learning Approach

Use embeddings + neural networks:

python
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import tensorflow as tf
from tensorflow.keras import layers

# Resource embeddings
resource_input = layers.Input(shape=(1,))
embedding = layers.Embedding(input_dim=num_resources, output_dim=50)(resource_input)
flatten = layers.Flatten()(embedding)

# Dense layers
dense = layers.Dense(64, activation='relu')(flatten)
output = layers.Dense(num_resources, activation='softmax')(dense)

model = tf.keras.Model(inputs=resource_input, outputs=output)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')

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Conclusion

A resource recommendation engine can be built with simple content-based methods or more advanced collaborative and hybrid systems. Start with TF-IDF for MVPs, scale with collaborative filtering as data grows, and later explore deep learning for personalization.

Let me know if you’d like this customized for a specific domain like education, e-learning, or tech resources.