Documents Search & Answer

Overview

This project focuses on search relevance: given a user query, rank candidate documents/results so that the most useful items appear at the top. The work combines information retrieval (IR) principles with learning-to-rank and neural re-ranking to improve end-to-end retrieval quality.

Typical applications: enterprise search, e-commerce search, knowledge-base search, and semantic search over large text collections.

Problem framing

We model search as a ranking task:

• Input: query + a set of candidate documents (title/body/metadata)
• Output: a ranked list, optimized for user satisfaction and business goals
• Key challenges: ambiguous queries, long-tail, synonyms, multilingual/typos, sparse clicks, position bias, cold-start

Approach (high level)

A practical relevance stack usually looks like this:

1. Retrieval (candidate generation)
   • lexical retrieval (e.g., BM25 / TF-IDF) and/or dense retrieval (embeddings)
2. Re-ranking (quality boost on top candidates)
   • gradient-boosted rankers (LambdaMART-style) and/or neural cross-encoders
3. Calibration & business rules (optional)
   • diversity, freshness, de-duplication, policy constraints

Signals & features

Examples of relevance signals that can be incorporated:

• Text relevance: query–title/body matches, term proximity, field boosts
• Semantic relevance: dense embeddings similarity, synonym/intent matching
• Behavioral signals: clicks, dwell time, add-to-cart / conversions (with debiasing)
• Document quality: authority, recency, popularity, spam indicators
• Query features: length, intent type, detected entities, language

Evaluation

Relevance work lives or dies by measurement. Common evaluation setup:

• Offline metrics: nDCG@k, MRR@k, MAP, Recall@k
• Human judgments: graded relevance labels (0–3) for query–doc pairs
• Online validation (if available): A/B tests, interleaving, CTR + downstream KPI

Deliverables / outputs

• A reproducible evaluation pipeline (datasets, splits, metrics)
• Baselines (lexical retrieval) + improved ranker (LTR / neural reranker)
• Error analysis: query classes, failure modes, feature importance, model drift checks

Visuals

One-liner (for CV)

Search relevance optimization: candidate retrieval + learning-to-rank / neural re-ranking, with rigorous offline evaluation (nDCG/MRR) and relevance-driven error analysis.