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Online stores and service providers rely heavily on recommendation softwaresto guide users through the vast amount of available products. Consequently, thefield of recommender systems has attracted increased attention from theindustry and academia alike, but despite this joint effort, the field stillfaces several challenges. For instance, most existing work models therecommendation problem as a matrix completion problem to predict the userpreference for an item. This abstraction prevents the system from utilizing therich information from the ordered sequence of user actions logged in onlinesessions. To address this limitation, researchers have recently developed apromising new breed of algorithms called sequence-aware recommender systems topredict the user's next action by utilizing the time series composed of thesequence of actions in an ongoing user session. This paper proposes a novelsequence-aware recommendation approach based on a complex network generated bythe hidden metric space model, which combines node similarity and popularity togenerate links. We build a network model from data and then use it to predictthe user's subsequent actions. The network model provides an additional sourceof information that improves the accuracy of the recommendations. The proposedmethod is implemented and tested experimentally on a large dataset. The resultsprove that the proposed approach performs better than state-of-the-artrecommendation methods.

A Sequence-Aware Recommendation Method Based on Complex Networks