Translation-based Recommendation

TL;DR

This paper introduces TransRec, a unified translation-based model that captures complex third-order interactions in sequential recommendation, outperforming existing methods on various real-world datasets.

Contribution

The paper proposes TransRec, a novel translation-based approach that models third-order user-item-item relationships in sequential recommendation tasks.

Findings

TransRec outperforms state-of-the-art methods on multiple datasets.

Embedding items in a transition space improves modeling of sequential behaviors.

Unified modeling of third-order interactions enhances recommendation accuracy.

Abstract

Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or `next-item' recommendation), where the challenges mainly lie in modeling `third-order' interactions between a user, her previously visited item(s), and the next item to consume. Existing methods typically decompose these higher-order interactions into a combination of pairwise relationships, by way of which user preferences (user-item interactions) and sequential patterns (item-item interactions) are captured by separate components. In this paper, we propose a unified method, TransRec, to model such third-order relationships for large-scale sequential prediction. Methodologically, we embed items into a `transition space' where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets. Data and code are available at https://sites.google.com/a/eng.ucsd.edu/ruining-he/.