NAIS: Neural Attentive Item Similarity Model for Recommendation (1809.07053v1)

Abstract: Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.

• The paper introduces NAIS, which leverages an attention mechanism to weight user history items distinctly in collaborative filtering.
• The model achieves a 4.5% relative improvement in NDCG performance compared to traditional approaches like FISM.
• NAIS employs a modified softmax function to stabilize attention weights across varied user interaction histories, enhancing recommendation accuracy.

Neural Attentive Item Similarity Model for Recommendation

The paper entitled "NAIS: Neural Attentive Item Similarity Model for Recommendation" introduces a novel approach to item-to-item collaborative filtering (CF) designed to enhance the effectiveness of recommender systems. The authors propose the Neural Attentive Item Similarity model (NAIS), which leverages an attention mechanism within a neural network framework to identify varying importance among items in a user's interaction history. This innovation addresses the limitations of traditional item-based CF methods, such as FISM, which treat all items in a user's history equivalently.

Key Contributions

The NAIS model stands out for its use of attention networks to derive item similarities, which allows for a more expressive and tailored representation of user preferences. The attention mechanism assigns different levels of importance to historical items, providing a weighted user profile representation rather than a uniform aggregation. The model subsumes the traditional FISM as a special case and offers enhanced representation power with minimal additional parameters.

Experimental Validation

The authors conducted extensive experiments using two public benchmark datasets to evaluate NAIS against FISM and other state-of-the-art recommendation techniques. The results demonstrate that NAIS achieves significant improvements in recommendation performance. Specifically, NAIS yields a relative improvement of 4.5% in NDCG, underscoring its superior ability to learn from user-item interaction data.

Technical Insights

NAIS employs a modified softmax function, addressing issues that arise when applying traditional attention mechanisms to datasets with significant variability in user interaction history length. This modification mitigates the large variance encountered in user histories, enabling more stable and meaningful attention weight calculations.

Implications and Future Directions

The introduction of NAIS opens new opportunities for advancing neural recommender systems. Its ability to improve upon existing item-based approaches highlights the potential of integrating attention networks into recommendation tasks, offering paths for further exploration such as deeper architectures or combining with graph-based methods.

This research advances theoretical insights into representation learning within CF systems and suggests practical enhancements for real-time personalization in industrial applications. Future work may focus on investigating more complex attention models or extending NAIS to factor in sequential patterns in user behavior.

Overall, NAIS provides a compelling advancement in the domain of collaborative filtering, offering both theoretical contributions and practical benefits that have considerable implications for the development of more sophisticated, user-aware recommender systems.