Automatic identification of user interest for personalized search

By Feng Qiu, Junghoo Cho. The Web Conference, 2006

517 citations recorded by the source corpus.

How researchers see this paper

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

Personalized Search (2015, background): “However, studies have shown that the vast majority of users are reluctant to provide any explicit feedback on search results and their interests [35] [45].”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “The average rank has been used in other papers [176, 208] to evaluate the effectiveness of the personalization strategy.”
A personalized search engine based on Web‐snippet hierarchical clustering (2005, background): “Qiu and Cho [56] suggest learning a user’s preferences automatically based on their past click history and show how to use this learning for result personalization.”
A large-scale evaluation and analysis of personalized search strategies (2007, methodology): “majority of queries to search engines are short [27, 12] and ambiguous [16, 7], and different users may have completely different information needs and goals under the same query [12, 26, 23, 34].”
Probabilistic factor models for web site recommendation (2011, unknown): “In this section, we review two research topics which are relevant to our work: Web user interests prediction and recommender systems.”
Exploring folksonomy for personalized search (2008, methodology): “Generally speaking, the evaluation methods used in prior personalized search studies fall into two categories, user experience study [21, 25, 22, 7, 8] and search engine query logs [10, 24].”