Handling user feedback and relevance feedback in Haystack involves a structured approach to gathering, processing, and integrating the insights into your search system. The goal is to improve the search experience by tuning the ranking of results based on what users find most relevant. Start by collecting user feedback through mechanisms such as feedback buttons, thumbs up/down ratings, or explicit surveys. This feedback allows you to understand how well the search results meet user expectations.
Once you have collected feedback, you need to analyze it. For relevance feedback, focus on the results the users found helpful or unhelpful. You can categorize this feedback into classes like "positive feedback" for items users clicked on or "negative feedback" for items they ignored. This data can then be transformed into training examples for your search model or algorithm. For instance, if users consistently rate certain documents as helpful for specific queries, you can adjust the ranking criteria to make similar documents more prominent in future searches.
Lastly, implement a feedback loop where user feedback is continuously incorporated into your system. In Haystack, you can utilize techniques like fine-tuning your retrievers and rankers based on user interactions. Using tools like Elasticsearch, you can adjust scoring algorithms or boost specific fields based on user preferences. Regularly updating your models with fresh user data will ensure your search maintains relevance over time, adapting to shifting user needs and improving overall satisfaction.