Prepare text representations
Collect item text (titles, summaries or full content), clean and normalise it, and convert to vector representations such as TF‑IDF or sentence embeddings.
01 / THE IDEA
Content recommendation in practice.
Content recommendation based on text uses document representations (for example TF‑IDF or sentence embeddings) to compute similarity between items and suggest related content. Important concerns include canonicalising text, handling short descriptions, controlling popularity bias, and offering diverse recommendations rather than near-duplicates.
Real-world use case · Proposed extension
A proposed application: recommend related articles or learning modules to readers based on the textual content they are viewing. In a production system, human editors should review automated suggestions and a logging pipeline must capture feedback for retraining.
02 / THE BUILD
How to build a content recommendation based on text project
step by step.
Compute similarity scores
Use cosine similarity on item vectors to form an item–item similarity matrix and identify top candidates for each item or user context.
Evaluate recommendations
Measure offline metrics such as precision@k, recall@k and diversity; manually inspect sample clusters for near-duplicates and topical drift.
Design safe deployment
Add business rules for freshness, diversity and content safety; implement fallback logic and a feedback loop to capture user interactions.
03 / SOURCE SNAPSHOT
What the content recommendation based on text starter actually does.
The supplied starter builds a small user–item ratings table, pivots it to a user-by-item matrix, computes cosine similarity between users and recommends items liked by the most similar user. It does not compute any text representations or use item text.
SOURCE EXCERPTPython
matrix = ratings.pivot_table(index="user", columns="item", values="rating").fillna(0)
user_similarity = pd.DataFrame(
cosine_similarity(matrix),
index=matrix.index,
columns=matrix.index,
)Important limitation
The starter operates on explicit user ratings and performs user-based collaborative filtering via cosine similarity; it contains no text preprocessing, no TF‑IDF or embedding step, and therefore does not implement the text-based recommendation described in the title.
04 / BEYOND THE CLASSROOM
Where a content recommendation based on text project could go next.
PROPOSED BUSINESS CONCEPT
Text-driven recommendation: a pilot concept
A proposed extension: replace the ratings table with item text, vectorise items using TF‑IDF or a sentence-embedding model, compute item–item similarity and serve topically related content with diversity filters.
WHO IT COULD SERVENews publishers or e-learning platforms
FIRST MVPA service that returns the top 5 text-similar items for a given article, with a small annotation UI for editors to accept or reject suggestions.
KEY RISKRecommendations may surface near-duplicate or stale content and could over-represent popular items; a human reviewer should remain in the loop and the system is not suitable for automated content-moderation decisions without human oversight.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a content recommendation based on text project puts on your resume.
- Understand how to convert item text into vector representations such as TF‑IDF or sentence embeddings.
- Compute cosine similarity to retrieve text-similar items and implement basic item–item recommendations.
- Measure precision@k and diversity to validate recommendation quality and inspect failure cases like near-duplicates.
- Explain that the supplied starter code uses ratings-based collaborative filtering and does not perform text-based similarity.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Content Recommendation Based on Text project FAQs.
- Is this suitable as a final year project?
- Yes. It’s a good applied final year project that combines NLP, information retrieval and evaluation — scope can be scaled from simple TF‑IDF to embeddings and diversity-aware ranking.
- What skills and tools will I need to complete it?
- Familiarity with Python, pandas, scikit‑learn, text preprocessing, TF‑IDF or sentence embeddings, cosine similarity and basic evaluation metrics will be useful; optional ML libraries for embeddings help.
- How should I evaluate or how long will it take?
- Evaluate using offline metrics like precision@k, recall@k, and diversity/novelty measures, plus qualitative case studies; a basic prototype can take 4–8 weeks, extensions longer.