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Query Chains: Learning to Rank from Implicit Feedback

Query Chains: Learning to Rank from Implicit Feedback 2026

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The paper presents a novel method for learning ranked retrieval functions from clickthrough data by utilizing query chains, which are sequences of related queries made by users. It demonstrates how to generate preference judgments from search engine logs based on user behavior, validating the approach through a controlled study and implementing it in a real-world search engine. The results show significant improvements in ranking performance compared to traditional methods, highlighting the effectiveness of incorporating implicit feedback from query chains.

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Begin by reviewing the abstract section, which outlines the purpose of the document. This will help you understand the context of your input.
Move on to the introduction section. Here, you can add notes or comments directly in the editor regarding your thoughts on query chains and their implications for search engines.
In the analysis of user behavior section, utilize text boxes to summarize key findings that resonate with your research or interests.
For sections discussing feedback strategies, highlight important strategies and annotate them with your insights using sticky notes available in our platform.
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What is an example of implicit feedback?

This is implicit because we only know that the customer bought the items, but we cant tell if they liked or which one they preferred. Some other examples of implicit feedback are the number of clicks, number of page visits, the number of times a song was played, etc

What is the difference between explicit and implicit recommendation?

One type of feedback is Explicit Feedback, which is the input of users regarding their interest in an item. This is the most helpful information since it directly comes from the user and shows their direct interest regarding the item. Implicit feedback is information produced after observing the users behavior.

What is recommendation uncertainty in implicit feedback recommender systems?

A Recommender Systems recommendations will each carry a certain level of uncertainty. The quantification of this uncertainty can be useful in a variety of ways. Estimates of uncertainty might be used externally; for example, showing them to the user to increase user trust in the abilities of the system.

What is item recommendation from implicit feedback?

The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only.

What is explicit and implicit feedback in recommender system?

Implicit and explicit feedback are two types of user data used in recommendation systems, differing primarily in how the information is collected and interpreted. Explicit feedback refers to direct, intentional input from users, such as ratings, reviews, or likes, where the user actively expresses their preference.