Evaluation problem: human judges may disagree on what’s relevant. Search engines model this with probabilistic labeling.
Read MoreRefers to unmodified baseline LMs (e.g., unigram/bigram) before fine-tuning. Often used as benchmarks in retrieval experiments.
Read MoreA training issue in deep networks where gradients disappear across layers. Relevant in training very deep ranking models (e.g., transformers).
Read MoreSearch engines measure variance/noise in click data to prevent overfitting rankings to random behaviors.
Read MoreEnsures neural rankers generalize well across domains by penalizing unstable parameter updates.
Read MoreUsed for learning latent document/query representations. Helps with unsupervised semantic clustering.
Read MoreBayesian method applied to ranking models → estimates uncertainty in predicted relevance.
Read MoreNeural models (e.g., Word2Vec, BERT, SBERT) map queries/documents into dense vectors. Used in semantic search, passage ranking, and MUM.
Read MoreCombining BM25 sparse vectors with dense embeddings for optimal retrieval.
Read MoreCompression technique used in ANN (Approximate Nearest Neighbor) search to make large embedding indexes (e.g., FAISS, ScaNN) efficient.
Read MoreVector search uses mathematical representations of content meaning rather than keywords. Optimize for vector search by creating semantically rich, contextually complete content that clearly conveys concepts.
Read MoreANN-based retrieval comparing query vs. doc embeddings. Core of modern semantic search systems.
Read MoreClassic IR model representing documents/queries as vectors in a high-dimensional space. Ranking is done by cosine similarity. Foundation for modern embedding-based retrieval.
Read MoreHybrid approach combining sparse inverted indexes (BM25) with vector embeddings for semantic ranking.
Read MoreGoogle blends verticals into the main SERP depending on query intent (e.g., “buy shoes” → Shopping + Ads + Organic).
Read MoreDomain-specific search systems (e.g., Google Images, News, Scholar, Amazon search). SEO strategies differ per vertical.
Read MoreVertical search refers to specialized search engines (images, news, shopping). Optimize specifically for each vertical (image alt text, product schema, news sitemaps).
Read MoreYahoo’s open-source vector search + hybrid retrieval engine. Frequently used for AI-powered search.
Read MoreGoogle uses vision + NLP to detect scenes, objects, and transcripts for key moments in video SERPs.
Read MoreOptimizing video metadata, transcripts, schema, and engagement signals to rank in Google Videos & YouTube.
Read MoreVideo SEO optimizes video content for search visibility. Include transcripts, optimize titles and descriptions, create video sitemaps, and use schema markup to help videos rank in both Google and YouTube.
Read MoreTracks whether content/ads are visible on-screen before interaction. Impacts ranking in Core Web Vitals & ad placement.
Read MoreGoogle’s Core Web Vitals metric for visual stability. Direct SEO ranking factor since 2021.
Read MoreViewport optimization ensures websites display correctly on all screen sizes. Set the viewport meta tag properly and use responsive design to provide optimal experiences across devices for better mobile rankings.
Read MoreViral coefficient measures content’s sharing potential and organic amplification. Create shareable content with emotional triggers, practical value, and social currency to earn natural links and visibility.
Read MoreVisibility measures how often a site appears in search results across keywords. Tools calculate visibility scores; aim to improve visibility for high-value terms.
Read MoreGoogle uses visit duration as a user satisfaction signal (short clicks = poor result, long clicks = useful).
Read MoreSearch powered by images instead of text (Google Lens, Pinterest Lens). SEO relies on structured image data + embeddings.
Read MoreDynamic programming algorithm used in HMMs to segment queries and disambiguate intent. Example: “apple watch price” → [Apple Watch] + [price].
Read MoreNeural methods for synonym generation and query-document bridging (e.g., “attorney” ↔ “lawyer”).
Read MoreOccurs when query words ≠ document words (synonyms, abbreviations). Dense retrieval and semantic matching are designed to solve this.
Read MoreReducing rarely used terms in an index to improve storage & speed. Impacts long-tail query recall.
Read MoreProcessing spoken queries, which often contain filler words or incomplete phrases. Google’s ASR + intent models normalize them.
Read MoreVoice search uses natural, conversational queries. Optimize by adding FAQ sections, using long-tail conversational phrases, and focusing on local intent for queries like “near me.”
Read MoreFrequent fluctuations in rankings. Tools like SEMrush Sensor & Algoroo measure SERP volatility.
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