iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering

TL;DR

This paper introduces iTrace, a trust-aware collaborative filtering algorithm that predicts implicit trust levels to improve recommendation accuracy by considering user credibility differences.

Contribution

It presents a novel trust inference method to estimate implicit trust and integrates it into CF, enhancing recommendation quality over traditional methods.

Findings

Significant reduction in MAE on a public dataset

Effective prediction of implicit trust levels

Improved recommendation accuracy

Abstract

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A collaborative filtering (CF) algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error (MAE).