research-article

Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization

Authors:

Jimmy Xiangji Huang,

Long XiaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 3

Article No.: 51, Pages 1 - 24

https://doi.org/10.1145/3476106

Published: 30 December 2021 Publication History

Abstract

In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.

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Cited By

Huang YHuang J(2024)Exploring ChatGPT for next-generation information retrieval: Opportunities and challengesWeb Intelligence10.3233/WEB-23036322:1(31-44)Online publication date: 26-Mar-2024
https://doi.org/10.3233/WEB-230363
He LZhao JGu YElbaz MDing Z(2024)A bias study and an unbiased deep neural network for recommender systemsWeb Intelligence10.3233/WEB-23003622:1(15-29)Online publication date: 26-Mar-2024
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Yuan WMa ZAn AHuang J(2024)Topic-aware response selection for dialog systemsNatural Language Processing Journal10.1016/j.nlp.2024.1000878(100087)Online publication date: Sep-2024
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Index Terms

Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 3

July 2022

650 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3498357

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 30 December 2021

Accepted: 01 July 2021

Revised: 01 July 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 3

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https://doi.org/10.3233/WEB-230036
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https://doi.org/10.1016/j.nlp.2024.100087
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