Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data

TL;DR

This paper introduces a deep learning-based unified cross-network recommendation system that addresses cold-start issues and optimizes ranking for implicit feedback, demonstrating superior performance on multiple datasets.

Contribution

It proposes a novel time-aware, listwise ranking approach for implicit data and a cross-network model leveraging auxiliary information to improve recommendation accuracy.

Findings

Outperforms baseline methods in accuracy, novelty, and diversity.

Effective in cold-start and dynamic user preference scenarios.

Listwise ranking method surpasses existing techniques on MovieLens.

Abstract

The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network base-lines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.