Get different results with query on search index

Question

Hello, I am performing a search on my data. So, the scenario is that -

1. I query from the UI to which the search service is connected.
2. I get a few results example- links
3. If now I query again asking to get me some more links, then I get the same links again. I am adding prompt also that I need new links but still I get the same links.

I understand that the search service returns results ranked on scores but is there a way I can get different results every time? Also using boosters and scoring profiles might not help right because it will narrow down the results fetched. I understand using these features will make the output more predictable right.

Answer 1

Hello Mansi Gusain,

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that your repeated search queries are returning identical results (links), even when the intent is to fetch new or varied links. Despite your understanding of how the search services typically rank results based on relevance scores and you want strategies to ensure different results with each query.

One effective way to prevent redundant links is by maintaining a record of previously retrieved results at the application level. Each time a new query is made, these stored results can be compared against the current response, and duplicates can be excluded before they are displayed to the user.

Implementation Details:

• Use session storage or an in-memory cache to track previously returned links.
• Apply filters in subsequent queries to exclude these stored links. For example, you can use Azure Cognitive Search’s filtering mechanism to avoid documents with specific IDs: in your https GET /indexes/{index}/docs?search={query}&$filter=not id in ('id1', 'id2', 'id3')

This ensures that the same links are not presented multiple times to the user.

Secondly, introducing session-based parameters in your queries is an effective way to ensure results differ across user interactions. Dynamic parameters like session IDs or timestamps can create query uniqueness and help the search engine return varied results by add a unique identifier to each query, such as a session ID: in http

GET /indexes/{index}/docs?search={query}&facet=sessionId&value={currentSessionId}

This method allows the search engine to associate results with the current session, helping it adapt to ongoing user interactions without repeating previous outputs.

Thirdly, pagination is an essential tool for fetching different parts of a search result set. Azure Cognitive Search’s $skip parameter lets you request results starting at a specific position in the ranked list. for an example:

First query: in http GET /indexes/{index}/docs?search={query}&$top=10

Next query to retrieve the following batch: in http GET /indexes/{index}/docs?search={query}&$skip=10&$top=10

By incrementing the $skip value, you can avoid repeating earlier results and progressively explore the entire dataset.

Fourthly, if the strict order of results is not critical, introducing randomness can diversify search results. Use this sparingly to avoid compromising relevance.

Create a custom scoring profile in Azure Cognitive Search to randomize results slightly. For instance,

{
  "scoringProfile": "randomize",
  "functions": [
    {
      "type": "magnitude",
      "fieldName": "randomField",
      "boost": 1,
      "interpolation": "linear"
    }
  ]
}

Randomness is introduced using a calculated randomField, which adds variability to the ranking.

Fifth, boosting content based on recency or user behavior can help ensure new and diverse results are displayed. This approach is particularly useful for dynamic datasets. To prioritize recent data, create a scoring profile that boosts documents indexed within a specific timeframe:

{
  "scoringProfile": "recentBoost",
  "functions": [
    {
      "type": "magnitude",
      "fieldName": "timestamp",
      "boost": 2,
      "interpolation": "linear"
    }
  ]
}

This scoring profile uses the timestamp field to ensure newer content is ranked higher.

Lastly, keeping the search index up-to-date is crucial for ensuring users receive fresh results. If your data source changes frequently, schedule updates to the Azure Cognitive Search index.

Use the Azure Search Data Importer or REST API to push updates dynamically: POST /indexes/{index}/docs/index

  {
    "value": [
      {
        "@search.action": "mergeOrUpload",
        "id": "123",
        "content": "New content here"
      }
    ]
  }

Frequent updates ensure the index reflects the most current state of your data, reducing redundancy.

I hope this is helpful! Do not hesitate to let me know if you have any other questions.

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