The Adaptive Web, Methods and Strategies of Web Personalization

By Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl. 2007

1,754 citations recorded by the source corpus.

Subfields: Management Information Systems

How researchers see this paper

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

In Management Information Systems

1. J. Webster
2. Jie Lu
3. Guandong Xu
4. Fangfang Li
5. Longbing Cao
6. Radhika Santhanam
7. Shilad Sen
8. Guangquan Zhang

Citations in the corpus

• A contextual-bandit approach to personalized news article recommendation (2010, background): “Recently, personalized recommendation has become a desirable feature for websites to improve user satisfaction by tailoring content presentation to suit individual users’ needs [10].”
• Personalized Search (2015, background): “from different services can be recommended based on news browsing [7].”
• Coupled Matrix Factorization Within Non-IID Context (2014, methodology): “Content-based techniques [12] are another successful methods which recommend relevant items to users according to users’ personal interests.”
• Recommender Systems (2016, neutral): “Discrete methods are common in the context of the Web domain, where the personalization needs to be performed with Web clickstreams [109].”
• Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness (2007, background): “Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness BAMSHAD MOBASHER, ROBIN BURKE, RUNA BHAUMIK, and CHAD WILLIAMS DePaul University Publicly accessible adaptive systems such as collaborative recommender systems present a security problem.”
• Recommender system application developments: A survey (2015, background): “Group recommender systems (GRS) are proposed to produce a group of user suggestions when group members are unable to gather for face-to-face negotiation, or their preferences are not clear in spite of meeting each other [66] [67].”
• Personalized recommendation on dynamic content using predictive bilinear models (2009, background): “Personalized recommendation has become a desirable feature of e-business Web sites to improve customer satisfaction and customer retention [8], by tailoring content presentation to suit an individual’s needs rather than take the traditional “one-size-fits-all” approach.”