LSTM Networks for Online Cross-Network Recommendations
This work improves cross-network recommendations for users by enabling online updates and better modeling of user interactions, though it is incremental as it builds on existing LSTM methods with specific enhancements.
The paper tackled the problem of cross-network recommender systems by addressing limitations in capturing non-linear relationships and online updates, proposing a novel multi-layered LSTM network with extensions like attention gating and time-aware gates, which outperformed state-of-the-art methods in accuracy, diversity, and novelty on a YouTube dataset using Twitter and Google Plus data.
Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues. The proposed model contains three main extensions to the standard LSTM: First, an attention gated mechanism to capture long-term user preference changes. Second, a higher order interaction layer to alleviate data sparsity. Third, time aware LSTM cell gates to capture irregular time intervals between user interactions. We illustrate our solution using auxiliary information from Twitter and Google Plus to improve recommendations on YouTube. Extensive experiments show that the proposed model consistently outperforms state-of-the-art in terms of accuracy, diversity and novelty.