Text-learning and related intelligent agents: a survey

By Dunja Mladenic. 1999

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Citations in the corpus

Adaptive interfaces and agents (2002, background): “Figure 15.12 shows part of a screen from the tourist information system AVANTI 2Surveys of parts of this large area are provided by, among others, Brusilovsky (1996, 2001), Hanani, Shapira, and Shoval (2001), and Mladenic (1999).”
Personalized recommendation on dynamic content using predictive bilinear models (2009, methodology): “Mladenic [30] provided a survey of the commonly used text-learning techniques in the context of content filtering.”
Recommender Systems (2016, unknown): “An early system, referred to as Personal WebWatcher [438, 439], makes recommendations by learning the interests of users from the Web pages that they visit.”
Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches (2005, methodology): “word tuples [7], We employed a shareware Rubryx”
Data Mining for Web Personalization (2007, methodology): “A survey of the commonly used text-learning techniques in the context of content-based ﬁltering can be found in [80].”
Diversity in recommender systems - A survey (2017, methodology): “Further development expanded these algorithms to a broader spectrum of content types from document search [6–9] to e-mail ﬁltering [10] and personalized multimedia item retrieval [11–15] .”
Content-based Recommender Systems: State of the Art and Trends (2011, background): “In the area of Web recommenders , famous systems in literature are Letizia [49], Personal WebWatcher [62, 63], Syskill & Webert [70, 68], ifWeb [4], Amalthea [66], and WebMate [23].”