A Sequence-Aware Recommendation Method Based on Complex Networks

TL;DR

This paper introduces a novel sequence-aware recommendation method using complex networks based on hidden metric space models, which improves prediction accuracy by leveraging user action sequences and network information.

Contribution

It presents a new recommendation approach that combines complex network modeling with sequence data, outperforming existing methods in accuracy.

Findings

The proposed method outperforms state-of-the-art recommendation algorithms.

Network-based modeling enhances prediction accuracy.

Experimental results validate the effectiveness of the approach.

Abstract

Online stores and service providers rely heavily on recommendation softwares to guide users through the vast amount of available products. Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges. For instance, most existing work models the recommendation problem as a matrix completion problem to predict the user preference for an item. This abstraction prevents the system from utilizing the rich information from the ordered sequence of user actions logged in online sessions. To address this limitation, researchers have recently developed a promising new breed of algorithms called sequence-aware recommender systems to predict the user's next action by utilizing the time series composed of the sequence of actions in an ongoing user session. This paper proposes a novel sequence-aware recommendation approach based on a complex network generated by the hidden metric space model, which combines node similarity and popularity to generate links. We build a network model from data and then use it to predict the user's subsequent actions. The network model provides an additional source of information that improves the accuracy of the recommendations. The proposed method is implemented and tested experimentally on a large dataset. The results prove that the proposed approach performs better than state-of-the-art recommendation methods.