Multiple User Context Inference by Fusing Data Sources

TL;DR

This paper introduces a novel method combining multiple data sources using tensor outer product and a probabilistic model to improve user context inference accuracy in recommender systems.

Contribution

It proposes a new data fusion technique and a probabilistic model for multi-attribute user context inference, addressing limitations of single-source and interdependence ignoring methods.

Findings

Outperforms existing models in recall, precision, and F1-measure.

Effectively fuses diverse data sources for better context inference.

Demonstrates scalability on large telecommunication datasets.

Abstract

Inference of user context information, including user's gender, age, marital status, location and so on, has been proven to be valuable for building context aware recommender system. However, prevalent existing studies on user context inference have two shortcommings: 1. focusing on only a single data source (e.g. Internet browsing logs, or mobile call records), and 2. ignoring the interdependence of multiple user contexts (e.g. interdependence between age and marital status), which have led to poor inference performance. To solve this problem, in this paper, we first exploit tensor outer product to fuse multiple data sources in the feature space to obtain an extensional user feature representation. Following this, by taking this extensional user feature representation as input, we propose a multiple attribute probabilistic model called MulAProM to infer user contexts that can take advantage of the interdependence between them. Our study is based on large telecommunication datasets from the local mobile operator of Shanghai, China, and consists of two data sources, 4.6 million call detail records and 7.5 million data traffic records of 8,000 mobile users, collected in the course of six months. The experimental results show that our model can outperform other models in terms of \emph{recall}, \emph{precision}, and the \emph{F1-measure}.