Implicit interest indicators

By Mark Claypool, Phong Le, M. Waseda, David Brown. International Conference on Intelligent User Interfaces, 2001

782 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e413d9b4d971facb08b24624ce625e9f03e4e78c

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In Information Systems

Citations in the corpus

Recommender systems in e-commerce (2014, background): “The amount of time a user spends on a particular page, reading and analyzing are implicit indicators of the user's ratings [7].”
Personalized recommendation on dynamic content using predictive bilinear models (2009, background): “measure various kinds of implicit feedback indicators from linger time [13] to eye movements [36].”
An MDP-Based Recommender System (2002, positive): “Claypool et al. (2001) have suggested the use of other implicit grading methods through a special web browser that keeps track of user behavior such as the time spent looking at the web page, the scrolling of the page by the user, and movements of the mouse over the page.”
Adaptive interfaces for ubiquitous web access (2002, background): “[5]) and not obtained exclusively from explicit content ratings provided by the user.”
Implicit user modeling for personalized search (2005, methodology): “(3) We propose a retrieval framework to integrate implicit user modeling with the interactive retrieval process, while the previous work either studies implicit user modeling separately from retrieval [3] or only studies specific retrieval models for exploiting implicit feedback to better match a qu”
Social media recommendation based on people and tags (2010, background): “To avoid this extra burden on the user, leveraging implicit interest indicators [6], such as purchase history, views, clicks, or queries, has recently become more popular in recommender systems.”
Collaborative filtering recommender systems (2013, background): “Implicit Profiling: Implicit profiling techniques build individual information by inferring users rating from so-called interest indicators depending on customer's interactions with the system (Claypool et al ., 2001; Goecks and Shavlik, 1999).”
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (2009, background): “Our classification of recommendation tasks sheds some light over another, commonly discussed subject in recommender systems, namely, implicit ratings (Claypool et al., 2001; Oard and Kim, 1998).”
Accurately interpreting clickthrough data as implicit feedback (2005, background): “The study presented in this paper is different in at least two respects from previous work assessing the reliability of implicit feedback [20, 6, 26, 8, 16].”
Collaborative Filtering: A Machine Learning Perspective (2004, neutral): “present an interesting comparison between implicit preference indicators and explicit ratings [15].”
Understanding web browsing behaviors through Weibull analysis of dwell time (2010, background): “[6] examined mouse clicks, scrolling, dwell time, and requested explicit ratings, and found that dwell time and the amount of mouse scrolling had a strong positive correlation with explicit ratings.”
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018, background): “There has been substantially more work on understanding implicit feedback signals in information retrieval (IR) [6, 11, 23, 31].”
A probabilistic method for inferring preferences from clicks (2011, background): “Many forms have been considered, such as the time spent on a page, scrolling, and clicks [5, 17].”
PocketLens: Toward a personal recommender system (2004, background): “Other researchers have also looked into the problem of using implicit information in a collaborative .ltering system to supplement or replace explicit preference information [Claypool et al. 2001; Terveen et al. 1997; Goecks and Shavlik 2002].”