Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the explosive growth of online social networks, it is now ll understood that social information is highly helpful to recommender systems. Social recommendation methods are capable of battling the critical cold-start issue, and thus can greatly improve prediction accuracy. The main intuition is that through trust and influence, users are more likely to develop affinity toward items consumed by their social ties. Despite considerable work in social recommendation, little attention has been paid to the important distinctions beten strong and ak ties, two ll-documented notions in social sciences. In this work,  study the effects of distinguishing strong and ak ties in social recommendation.  use neighbourhood overlap to approximate tie strength and extend the popular Bayesian Personalized Ranking (BPR) model to incorporate the distinction of strong and ak ties.  present an EM-based algorithm that simultaneously classifies strong and ak ties in a social network w.r.t. optimal recommendation accuracy and learns latent feature vectors for all users and all items.  conduct extensive empirical evaluation on four real-world datasets and demonstrate that our proposed method significantly outperforms state-of-the-art pairwise ranking methods in a variety of accuracy metrics.
['Xin Wang', 'Wei Lu', 'Martin Ester', 'Can Wang', 'Chun Chen']
I am writing a research paper on a idea Social Recommendation with Strong and Weak Ties
[{"from": "human", "value": "I am writing a research paper on a idea Social Recommendation with Strong and Weak Ties"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the explosive growth of online social networks, it is now ll understood that social information is highly helpful to recommender systems. Social recommendation methods are capable of battling the critical cold-start issue, and thus can greatly improve prediction accuracy. The main intuition is that through trust and influence, users are more likely to develop affinity toward items consumed by their social ties. Despite considerable work in social recommendation, little attention has been paid to the important distinctions beten strong and ak ties, two ll-documented notions in social sciences. In this work,  study the effects of distinguishing strong and ak ties in social recommendation.  use neighbourhood overlap to approximate tie strength and extend the popular Bayesian Personalized Ranking (BPR) model to incorporate the distinction of strong and ak ties.  present an EM-based algorithm that simultaneously classifies strong and ak ties in a social network w.r.t. optimal recommendation accuracy and learns latent feature vectors for all users and all items.  conduct extensive empirical evaluation on four real-world datasets and demonstrate that our proposed method significantly outperforms state-of-the-art pairwise ranking methods in a variety of accuracy metrics."}]