Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recommendation system, the ratings represent the users' preference and play an important role in recommending items to users. Hover, the ratings of items may be influenced by many factors, such as time the latest ratings are more able to reflect the user's current preferences, user familiarity the more familiar a user to an item, the more reliable of rating he gives. So ratings should have different recommended ights in different circumstances. Hover, current recommendation algorithms ignore this problem and use the ratings indiscriminately, this affecting the accuracy of the recommendation system. ,  proposed a general rating recommended ight-aware model, which can fuse all kinds of recommended ights naturally for item recommendation.  design a new rating ight-aware probability matrix factorization model, which can assign recommended ight to every rating to obtain precise recommendations.  conduct comprehensive experiments using the real-world datasets. Experimental results show that the rating-aware recommendation model outperforms state-of-the-art latent factor models with a significant margin.
['Haihong E', 'Yusheng Li', 'Xuejun Zhao', 'Meina Song', 'Junde Song']
I am writing a research paper on a idea A General Rating Recommended Weight-Aware Model for Recommendation System
[{"from": "human", "value": "I am writing a research paper on a idea A General Rating Recommended Weight-Aware Model for Recommendation System"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recommendation system, the ratings represent the users' preference and play an important role in recommending items to users. Hover, the ratings of items may be influenced by many factors, such as time the latest ratings are more able to reflect the user's current preferences, user familiarity the more familiar a user to an item, the more reliable of rating he gives. So ratings should have different recommended ights in different circumstances. Hover, current recommendation algorithms ignore this problem and use the ratings indiscriminately, this affecting the accuracy of the recommendation system. ,  proposed a general rating recommended ight-aware model, which can fuse all kinds of recommended ights naturally for item recommendation.  design a new rating ight-aware probability matrix factorization model, which can assign recommended ight to every rating to obtain precise recommendations.  conduct comprehensive experiments using the real-world datasets. Experimental results show that the rating-aware recommendation model outperforms state-of-the-art latent factor models with a significant margin."}]