A large-scale evaluation and analysis of personalized search strategies

By Zhicheng Dou, Ruihua Song, Ji-rong Wen. The Web Conference, 2007

703 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/366862ca872ad9ddba0183d922be385b0ed0df1e

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In Computer Vision and Pattern Recognition

In Information Systems

In Signal Processing

Citations in the corpus

Take This Personally: Pollution Attacks on Personalized Services (2013, background): “are not public, but many previous studies have explored aspects of personalized search [2,4,6,7,9,10,14–18].”
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, neutral): “Notably, in the literature of personalized search [1, 10], there are various ways to inject personalization into traditional search engines, such as introducing user’s historical requests or clicks in logs.”
Measuring personalization of web search (2013, background): “Dou et al. provide a comprehensive overview of techniques for personalizing search [14].”
Modeling the impact of short- and long-term behavior on search personalization (2012, methodology): “Finally, the large increase in MAP in the first position for the methods using long-term information suggests that more ambiguous queries, which personalization most benefits [11][30], may be more likely to happen as the first query in the session.”
Measuring Price Discrimination and Steering on E-commerce Web Sites (2014, background): “provide a comprehensive overview of techniques for personalizing search [12].”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “leveraging browsing history or even e-mail conversations (Teevan et al., 2005; Dou et al., 2007; Matthijs and Radlinski, 2011).”
Applications of Topic Models (2017, positive): “Eidelman et al. [2012] combine the standard f(f̄ |ē) and f(ē|f̄) with two directions of topic-adapted probabilities pw(f̄ |ē; a, k) and pw(ē|f̄ ; a, k), equivalent to introducing 2K new word translation tables.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “5: Click entropy distribution for the MSN query log [73].”
To personalize or not to personalize: modeling queries with variation in user intent (2008, background): “‟s [5] work is quite relevant since it examines behavioral variability in the context of personalizing search results.”
Click Models for Web Search (2015, background): “Dupret and Piwowarski [2008] are the first to point out this application of click models.”