Implicit feedback for inferring user preference: a bibliography

By Diane Kelly, Jaime Teevan. SIGF, 2003

848 citations recorded by the source corpus.

Subfields: General Health Professions, Philosophy

View Paper: https://www.semanticscholar.org/paper/235cf387b1a597078c9d9c1cbc794dd1e37b8dc3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Health Professions

In Philosophy

Citations in the corpus

Determining the informational, navigational, and transactional intent of Web queries (2008, result): “This approach has much in common with research on implicit feedback (Jansen, 2005, 2006; Jansen & McNeese, 2005; Kelly & Belkin, 2001, 2004; Kelly & Teevan, 2003; Oard & Kim, 2001), where one attempts to use other expressions of the user as forms of relevance judgments.”
Implicit user modeling for personalized search (2005, background): “Unfortunately, in real world applications, users are usually reluctant to make the extra effort to provide relevant examples for feedback [11].”
Personalizing search via automated analysis of interests and activities (2005, background): “Kelly and Teevan [11] review research on the use of implicit measures to improve search, highlighting several approaches in the literature that seek to tailor results for individuals.”
Improving web search ranking by incorporating user behavior information (2006, neutral): “An overview of implicit measures is compiled in Kelly and Teevan [14].”
Potential for personalization (2010, background): “Clickthrough is a common behavior-based proxy for relevance [Kelly and Teevan 2003].”
Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks (2015, background): “In a real recommendation system, there are usually at least two types of implicit feedbacks [10,16], e.”
Exploratory Search: Beyond the Query-Response Paradigm (2009, background): “Research on implicit feedback ( Joachims et al., 2005; Kelly and Belkin, 2004; Kelly and Teevan, 2003; White et al., 2005b) has shown that interaction behavior (mainly document retention activities such as saving, bookmarking, and printing, as well as search engine result page”
Improving Web Search Ranking by Incorporating User Behavior Information (2019, neutral): “An overview of implicit measures is compiled in Kelly and Teevan [14].”
Learning user interaction models for predicting web search result preferences (2006, background): “A more comprehensive overview of studies of implicit measure s is described in Kelly and Teevan [12].”
How does clickthrough data reflect retrieval quality? (2008, neutral): “Kelly & Teevan give an overview [15].”
Finding the right facts in the crowd: factoid question answering over social media (2008, result): “Our work is similar in spirit to integrating user interactions and feedback into web search [12, 13, 15, 1].”
Changing “Course” (2014, background): “The time spent on a web page is notoriously difficult to measure in online environments (Clifton, 2012), let alone the amount of time that a user is actually attending to the content (Kelly & Teevan, 2003).”
Some(what) grand challenges for information retrieval (2008, methodology): “Most research in this area is concerned with identifying an aspect of current or past behaviors, such as dwell time on a page, click-through, information objects which have been previously been viewed or saved or used, and using that information as a form of relevance feedback, to either modify in s”
Online Evaluation for Information Retrieval (2016, background): “The first key to the definition is implicit measurement, which we take to include any measurements that can be derived from observable user activity that is part of users’ natural or normal interaction with the system [Kelly and Teevan, 2003].”
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (2019, background): “g layer as a N-layer fully-connected neural network. The output layer, which is the subsequent layer of the decision-making layers, estimates user preferences that could be ratings, implicit feedback [7], or dwell times [29]. The layers can be formulated as x0 = [Ui;Ij], x1 = a(W ⊺ 1 x0 +b1), .. . x”