Exploring the use of Time-Dependent Cross-Network Information for Personalized Recommendations
This work addresses personalized recommendation challenges for users in online applications, but it is incremental as it builds on existing cross-network and time-aware methods.
The paper tackled incomplete user profiles and dynamic preferences in recommendations by proposing a cross-network time-aware solution that aggregates user preferences from multiple source networks and learns time-aware latent factors, achieving superior performance in accuracy, novelty, and diversity compared to baselines.
The overwhelming volume and complexity of information in online applications make recommendation essential for users to find information of interest. However, two major limitations that coexist in real world applications (1) incomplete user profiles, and (2) the dynamic nature of user preferences continue to degrade recommender quality in aspects such as timeliness, accuracy, diversity and novelty. To address both the above limitations in a single solution, we propose a novel cross-network time aware recommender solution. The solution first learns historical user models in the target network by aggregating user preferences from multiple source networks. Second, user level time aware latent factors are learnt to develop current user models from the historical models and conduct timely recommendations. We illustrate our solution by using auxiliary information from the Twitter source network to improve recommendations for the YouTube target network. Experiments conducted using multiple time aware and cross-network baselines under different time granularities show that the proposed solution achieves superior performance in terms of accuracy, novelty and diversity.