Implicit Feedback for Recommender Systems

By Douglas W. Oard, Jinmook Kim. 1998

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Citations in the corpus

Deep Neural Networks for YouTube Recommendations (2016, methodology): “) we use the implicit feedback [16] of watches to train the model, where a user completing a video is a positive example.”
Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings (2006, methodology): “Two methods also exist for collecting user preference data—implicit and explicit [ 33 , 51].”
Collaborative Filtering for Implicit Feedback Datasets (2008, background): “Thus, recommenders can infer user preferences from the more abundant implicit feedback, which indirectly reflect opinion through observing user behavior [14].”
Recommendation systems: Principles, methods and evaluation (2015, background): “Recommender systems rely on different types of input such as the most convenient high quality explicit feedback, which includes explicit input by users regarding their interest in item or implicit feedback by inferring user preferences indirectly through observing user behavior [31].”
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).”
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.”
Collaborative Filtering Recommender Systems (2011, background): “Preferences can also be inferred from user behavior [106].”
Trust in recommender systems (2005, background): “These problems are more thoroughly discussed in [ 15 ]”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Some nonintrusive methods of getting user feedback are presented in [18], [53], [66], [74], [94].”
Recommender Systems (2016, unknown): “These data sets are also referred to as implicit feedback data sets [260, 457].”
Implicit interest indicators (2001, background): “Oard and Kim [14] build upon work by Nichols [13] by categorizing implicit ratings, dividing them into \Examination", where a user studies an item, \Retention" where a user saves an item for later use, and \Reference" where a user links all or part of an item into another item.”
Content-Based Collaborative Filtering for News Topic Recommendation (2015, methodology): “Implicit Feedback is originated from the area of information retrieval and the related techniques have been successfully applied in the domain of recommender systems (Kelly and Teevan 2003; Rendle et al. 2009; Koren 2008; Oard, Kim, and others 1998).”
Collaborative Filtering Recommender Systems (2007, background): “Other examples of implicit ratings are discussed in Oard and Kim [45].”
Understanding and improving automated collaborative filtering systems (2000, background): “Oard presents a taxonomy of different methods of observable behavior for implicit ratings[65].”