feat: replace hardcoded collaborative filter in `/recommend-events` with item-based similarity scoring

[BigBen-7]

Description

/recommend-events uses a toy user-based collaborative filter on a 4-user mock dict. The algorithm counts user-pair overlaps but ignores event popularity, recency, or ticket availability. Item-based similarity (events that are frequently bought together) is more robust for cold-start users and scales better.

Requirements & context

• Implement build_item_similarity_matrix(user_events_dict) in a new src/recommender.py using cosine similarity between event co-occurrence vectors
• Replace the current scoring loop in /recommend-events with a call to get_item_recommendations(user_id, user_events_dict, similarity_matrix, top_n=3)
• The function must handle cold-start: if the user has no purchase history, return the 3 most popular events (highest total purchase count)
• The user_events_dict should come from the DB (real user scan/purchase history) not the mock — use get_user_events_from_db from the earlier search fix
• Write unit tests with a known similarity matrix asserting correct top-3 output

Suggested execution

git checkout -b feat/item-based-recommendation

• Create src/recommender.py
• Update src/main.py /recommend-events handler
• Write tests

Guidelines

• Do not use numpy for this — pure Python dict-based cosine similarity is sufficient and avoids a heavy dependency
• PR must include: Closes #[issue_id]
• Timeframe: 72 hours

[portableDD]

@portableDD has applied to work on this issue as part of the Stellar Wave Program's 3rd wave.

can i work on this?

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