Personalizing web search results based on subspace projection

Li, Jingfei; Song, Dawei; Zhang, Peng; Wen, Ji-Rong and Dou, Zhicheng (2014). Personalizing web search results based on subspace projection. In: Information Retrieval Technology, Lecture Notes in Computer Science, Springer, pp. 160–171.

DOI: https://doi.org/10.1007/978-3-319-12844-3_14

Abstract

Personalized search has recently attracted increasing attention. This paper focuses on utilizing click-through data to personalize the web search results, from a novel perspective based on subspace projection. Specifically, we represent a user profile as a vector subspace spanned by a basis generated from a word-correlation matrix, which is able to capture the dependencies between words in the “satisfied click” (SAT Click) documents. A personalized score for each document in the original result list returned by a search engine is computed by projecting the document (represented as a vector or another word-correlation subspace) onto the user profile subspace. The personalized scores are then used to re-rank the documents through the Borda’ ranking fusion method. Empirical evaluation is carried out on a real user log data set collected from a prominent search engine (Bing). Experimental results demonstrate the effectiveness of our methods, especially for the queries with high click entropy.

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