How does Artificial Intelligence and semantic search work for a portal

This is a very vague question .. and sorry to say I wont be able to answer you in a better way...

For the sake of conversation , lets say you own clarity.fm and see how we can fit semantic and AI on to it...

I had once started a similar idea in past on yelp reviews..

Semantic - In layman langugae, you are breaking down large language pieces to small sentences and making a meaning out of it...and tagging them..
E.g If there was an sematic alogorithm you had run on this particular question , then the results would be
Vagueness = 10, topic = technology, industry = web etc..

TO get this done, there should be a lexicon database that you should seed and curate.

AI - This is more about applying data science and doing doing deep linking, mapping between all the raw data points (like User details) and derived information (like semantics) and identifying patterns. This is just one example.

One practical example using clarity as case study using this clarity question --- If today you want to make a call for some help, calrity can use its AI algorithms to to identify who would be the best person to answer this..(based on the answers made in the past).

This is a vast topic...
To have a better answer , you need to share more info..
Feel free to talk. Not sure where you are based but if you can't afford the $, ask from me for a free call.. more than happy to help for 30 mins.. love the challanging part.

Great question! AI and semantic search can dramatically enhance the user experience and efficiency of an online marketplace portal by making search results more accurate, relevant, and user-friendly. Let’s break it down:

What Is Semantic Search?
Semantic search goes beyond keyword matching. It understands the intent behind a query and the contextual meaning of terms. Instead of just looking for exact words, it interprets what the user really wants.

How AI + Semantic Search Work for a Marketplace Portal
1. Understanding the User’s Query (Natural Language Processing)
AI models (like BERT, GPT, etc.) parse the user's search query to understand intent, entities, context, and relationships between words.

Example: A user searches for “eco-friendly office chair under $200.”

AI understands:
“eco-friendly” → attribute
“office chair” → product type
“under $200” → price constraint

2. Matching Products Intelligently (Semantic Matching)
AI uses vector embeddings to map both the query and product listings into a high-dimensional space.

Even if the user and product listings use different wording, the system finds semantically similar matches.

“Affordable green chair for work” might still return results tagged as “budget eco-conscious ergonomic chairs.”

3. Product Ranking & Recommendations (AI Ranking Algorithms)
AI models rank products by relevance, popularity, availability, reviews, and personalization.

Factors include:
Past behavior (clicks, purchases)
Product metadata
Business goals (e.g., higher margin items)

4. Auto-Suggestions and Autocomplete
As the user types, AI provides intelligent autocomplete suggestions based on trending searches, user intent, and semantic understanding.

5. Context-Aware Filters and Facets
Instead of showing generic filters, the portal dynamically adjusts filter options based on the category and intent behind the query.

6. Personalized Search & Recommendations
AI learns from user behavior over time:
Search history
Click patterns
Purchase history

7. Multimodal Search (Optional)
Some marketplaces integrate visual search (e.g., uploading an image) or voice search. AI interprets those inputs and returns semantically relevant results.

If you tell me more about your specific marketplace (e.g., fashion, electronics, niche products), I can tailor the explanation even more.