NPA: Neural News Recommendation with Personalized Attention (1907.05559v1)

Abstract: News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.

Overview of Neural News Recommendation with Personalized Attention

The paper, "NPA: Neural News Recommendation with Personalized Attention," presents an approach to address the challenges of personalized news recommendation. The authors propose a model that captures the dynamic and individualized nature of user interests, utilizing a Neural News Recommendation model supported by Personalized Attention (NPA).

Core Contributions

The central innovation of the NPA model is its dual-layered attention mechanism: a word-level personalized attention and a news-level personalized attention. These attentions allow the model to focus on the most informative terms within news articles and identify pertinent news articles for different users, respectively. Here are the key components of the NPA model:

1. News Representation Model: It employs a Convolutional Neural Network (CNN) to extract contextual features from news articles based on their titles. Unlike traditional news recommendation systems that rely on predefined features, the CNN architecture enables the automatic extraction of meaningful patterns from words within the title.
2. User Representation Model: It constructs representations of users by analyzing their interaction history with certain news articles. This is critical as it accounts for the user's historical preferences in generating recommendations.
3. Personalized Attention Mechanism: Central to the NPA model, personalized attention mechanisms function at both the word and news levels. The word-level attention identifies salient words within news titles that are especially informative for news topic representation. The news-level attention ascertains which of the user's previously clicked articles most accurately reflect their current preferences.
4. Training with Negative Sampling: The model incorporates a negative sampling approach to efficiently manage the vast amount of negative instances when calculating recommendation likelihoods, enhancing both computational efficiency and model performance.

Experimental Evaluation

The authors implemented the NPA model on a dataset collected from MSN News, exhibiting its superior performance over traditional CF methods and other neural approaches. Notably, the model's AUC, MRR, nDCG@5, and nDCG@10 scores showed a marked improvement, indicating a more accurate prediction of user interest in news articles. The utilization of personalized attention mechanisms proved effective in synthesizing differentiated importance among words and news articles based on user preferences, further corroborating the theoretical underpinnings of the paper.

Implications and Future Considerations

The introduction of the NPA model marks a significant advancement in news personalization technology, unlocking potential improvements in recommendation systems. Methodologically, it underscores the importance of personalized attention mechanisms in creating accurate user profiles and highlights the utility of neural networks in automatic feature extraction from textual data.

Future research might explore leveraging more nuanced attention mechanisms, potentially integrating real-time user data from social media platforms to capture dynamic shifts in user interests. Additionally, refining the computational cost associated with such complex models remains a pertinent area of optimization to ensure scalability and responsiveness in real-time environments.

In conclusion, the NPA model provides a robust framework for enhancing user engagement through personalized news recommendations, contributing meaningfully to the literature and practices within intelligent information retrieval and user modeling. The personalized attention approach has implications beyond news recommendation, suggesting utility in diverse domains where user preference and content specificity are crucial.