User Modeling for Adaptive News Access

By Daniel Billsus, Michael J. Pazzani. User modeling and user-adapted interaction, 2000

594 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/506b7c8056d3aa3c1fd73e75ed36658f03a392e0

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Citations in the corpus

Ontological user profiling in recommender systems (2004, methodology): “Personal web-based agents such as NewsDude and Daily Learner [Billsus and Pazzani 2000], Personal WebWatcher [Mladenić 1996] and NewsWeeder [Lang 1995] build profiles from observed user behaviour.”
Recommender systems survey (2013, background): “Billus and Pazzani [25] propose a recommendation system on news, which provides keyword style justifications of the recommendations through the weights used for obtaining these recommendations .”
A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011, methodology): “For items in the form of text documents, such as news articles [8, 44] or Web documents [7, 58], this vector often contains the Term Frequency-Inverse Document Frequency (TFIDF) [65] weights of the most informative keywords.”
Learning to model relatedness for news recommendation (2011, background): “In news domain, recommendation services often rely on user activities such as click patterns to find the most popular news [2], user-news ratings for collaborative news recommendation [25, 17, 10], or user interests for content-based and adaptive news access [4].”
Emotion recognition from physiological signals using wireless sensors for presence technologies (2004, background): “The applications of this traditional user modeling include student modeling [1] [8] [25] [30], news access [ 2 ], e-commerce [13], and health-care [31].”
Recommender Systems (2016, unknown): “Methods for performing news recommendations are discussed in [41, 84, 85, 392, 543, 561].”
Adaptive interfaces for ubiquitous web access (2002, background): “In a visit to a wireless site, users read over 40% more content that is personalized to their interests [2].”
A survey of multiple classifier systems as hybrid systems (2014, background): “Billsus and Pazzani [228] selected the best recommendation on the basis of a”
Personal recommendation using deep recurrent neural networks in NetEase (2016, methodology): “Two popular recommendation techniques are applied, the CF approach (Collaborative Filtering) [20][13] and the content-based approach [3][28].”
Recommender system application developments: A survey (2015, background): “According to Burke [27], there are seven basic hybridization mechanisms of combinations used in recommender systems to build hybrids: weighted [28], mixed [29], switching [30], feature combination, feature augmentation [31] [32], cascade [14] and meta-level [33].”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Therefore, some content-based recommender systems, such as Daily-Learner [13], filter out items not only if they are too different from the user’s preferences, but also if they are too similar to something the user has seen before.”
Music recommendation and discovery in the long tail (2008, background): “Other proposals include disregarding items if they are too similar to other items that the user has already seen (Billsus and Pazzani, 2000), or simple metrics to measure novelty and serendipity based on the average popularity of the recommended items Ziegler et al.”
A unified approach to building hybrid recommender systems (2009, background): “” Other approaches include combining the results of different recommenders by weighting the resulting scores [8] or voting [23], switching between different recommenders [4], and filtering or reranking the results of one recommender with another [6].”
Using Topic Models in Content-Based News Recommender Systems (2013, methodology): “Billsus and Pazzani (2000) used a combination of Nearest Neighbor Classiﬁcation and Naïve Bayes classiﬁcation for explicit feedback in an interesting / not interesting classiﬁcation.”
Hybrid Web Recommender Systems (2007, background): “There is no reason why several different techniques of the same type could not be hybridized, for example, two different content-based recommenders could work together, and a number of projects have investigated this type of hybrid: NewsDude, which uses both naive Bayes and kNN classifiers in its ne”
Challenges and Solutions of Ubiquitous User Modeling (2012, background): “It was noted that such a stereotype could be used to reason across domains, for example, taking a model of the user’s preferences for books and using this to infer a model for the their preferences for movies.”