Translation-based Recommendation (1707.02410v1)

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 modelingthird-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/.

• The paper introduces TransRec, a translation-based model that embeds items in a latent transition space to capture third-order user-item interactions.
• It employs user translation vectors to blend sequential dynamics with preference data, surpassing traditional pairwise methods like Matrix Factorization and Markov Chain models.
• Extensive experiments on diverse datasets confirm its robustness and scalability, showing significant improvements in metrics like AUC and Hit@50, especially in sparse contexts.

Insights into "Translation-based Recommendation"

"Translation-based Recommendation," authored by Ruining He, Wang-Cheng Kang, and Julian McAuley, presents a sophisticated approach to handling the personalized sequential prediction problem inherent in modern recommender systems. This research introduces TransRec, a model emphasizing a translation-based framework to capture third-order interactions—essentially user preferences along with item-item sequential dynamics—in contrast to traditional methods that decouple these into lower-order pairwise combinations.

Key Contributions and Methodology

The core innovation of TransRec lies in embedding items into a latent 'transition space,' where users are modeled as translation vectors that apply transformations to sequences of items. By doing so, the model circumvents the complexity and limitations posed by traditional methods like Matrix Factorization (MF) and Markov Chain (MC) models, which typically deal with only pairwise interactions. The crux of TransRec's design is to integrate the user's influence and the sequential dynamics of items through this translation vector, enhancing the model's capacity to predict the next item in a sequence accurately.

TransRec endeavors to optimize the prediction of future user actions by leveraging translation operations that place the resultant transformed vector close to the latent vector of the next item. This approach is bolstered by a metric space where distances reflect transition probabilities, aligning closely with techniques shown effective in knowledge graph completion tasks.

Empirical Findings

Evidence from extensive experiments across diverse datasets, including those from Amazon categories, Epinions, and the newly introduced Google Local, underscores TransRec's superiority in both sparsity and variability contexts. Notably, it achieves significant performance enhancements in AUC and Hit@50 metrics, often outperforming state-of-the-art models like FPMC, PRME, and HRM. The model's strength particularly shines through in sparse datasets, demonstrating robust generalization capabilities despite the inherent data sparsity.

TransRec's application is not limited to user-item predictions but extends to item-to-item recommendations, a task drawing parallels with knowledge graph completion. The empirical results showcase its effectiveness over leading methods like Monomer and LMT, highlighting the potential of translation structures in capturing more intricate cross-item relationships.

Theoretical and Practical Implications

The merger of user preferences with sequential dynamics through a unified translation operation offers substantial theoretical implications. It questions the necessity of decomposing complex interactions into simpler pairwise forms and suggests that metric spaces can encapsulate higher-order interactions in a more integrated fashion. Practically, the model's simplicity and scalability make it well-poised for real-world applications, where handling vast data efficiently is crucial.

Future Directions

Future research avenues could explore integrating TransRec with more complex neural architectures or incorporating contextual and temporal information to further enrich its predictive capabilities. Additionally, expanding its applicability to more diverse domains and enhancing its interpretability could cater to more nuanced user understandings and preferences.

In summary, "Translation-based Recommendation" provides a compelling advance in recommendation system methodologies by revisiting and refining the conceptual barriers of modeling user-item interactions. By embedding users and items within a translation framework, it sets a precedent for future explorations in the continuous improvement of sequential prediction models.