FE-TCM: Filter-Enhanced Transformer Click Model for Web Search

TL;DR

This paper introduces FE-TCM, a novel Transformer-based click model with filter layers that effectively reduces noise and improves click prediction accuracy in web search by modeling attraction and examination factors.

Contribution

The paper proposes a new FE-TCM model that integrates learnable filters with Transformer architecture to enhance click prediction in web search.

Findings

FE-TCM outperforms existing click models in experiments.

Learnable filters effectively reduce noise in user behavior data.

Transformer-based feature extraction improves representation of user interactions.

Abstract

Constructing click models and extracting implicit relevance feedback information from the interaction between users and search engines are very important to improve the ranking of search results. Using neural network to model users' click behaviors has become one of the effective methods to construct click models. In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output the attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.