Personalized and Diversified: Ranking Search Results in an Integrated Way
Authors: 
Shuting Wang, 
Zhicheng Dou, Jiongnan Liu, Qiannan Zhu, and Ji-Rong WenAuthors Info & Claims
Shuting Wang, Zhicheng Dou, Jiongnan Liu, Qiannan Zhu, and Ji-Rong WenAuthors Info & ClaimsArticle No.: 81, Pages 1 - 25
Abstract
Ambiguity in queries is a common problem in information retrieval. There are currently two solutions: search result personalization and diversification. The former aims to tailor results for different users based on their preferences, but the limitations are redundant results and incomplete capture of user intents. The goal of the latter is to return results that cover as many aspects related to the query as possible. It improves diversity yet loses personality and cannot return the exact results the user wants. Intuitively, such two solutions can complement each other and bring more satisfactory reranking results. In this article, we propose a novel framework, namely, PnD, to integrate personalization and diversification reasonably. We employ the degree of refinding to determine the weight of personalization dynamically. Moreover, to improve the diversity and relevance of reranked results simultaneously, we design a reset RNN structure (RRNN) with the “reset gate” to measure the influence of the newly selected document on novelty. Besides, we devise a “subtopic learning layer” to learn the virtual subtopics, which can yield fine-grained representations of queries, documents, and user profiles. Experimental results illustrate that our model can significantly outperform existing search result personalization and diversification methods.
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Publication History
Published: 22 January 2024
Online AM: 09 November 2023
Accepted: 17 October 2023
Revised: 08 July 2023
Received: 18 June 2022
Published in TOIS Volume 42, Issue 3
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