Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender is a personalized service in the adaptive information system, and it can provide personalized information according to individual information needs. As one of the known technology in the field of the recommender systems, collaborative filtering has been widely used in E-Commerce for its advantages. But the rating prediction mechanism of pure collaborative filtering is merely based on the ratings for visited items, and this limits its precision improvement. ,  propose a refined hybrid recommender approach based on Widrow-Hoff learning algorithm. The proposed approach employs Widrow-Hoff algorithm to learn each user?s profile from the contents of rated items, to improve the granularity of the user profiling. With the refined user profiles, collaborative filtering is employed to compute more precise similarity of different users, and predicts the ratings for unrated items. The improvement of performance is demonstrated by the experimental evaluation.
['Lei Ren', 'Liang He', 'Junzhong Gu', 'Weiwei Xia', 'Faqing Wu']
I am writing a research paper on a idea A Hybrid Recommender Approach Based on Widrow-Hoff Learning
[{"from": "human", "value": "I am writing a research paper on a idea A Hybrid Recommender Approach Based on Widrow-Hoff Learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender is a personalized service in the adaptive information system, and it can provide personalized information according to individual information needs. As one of the known technology in the field of the recommender systems, collaborative filtering has been widely used in E-Commerce for its advantages. But the rating prediction mechanism of pure collaborative filtering is merely based on the ratings for visited items, and this limits its precision improvement. ,  propose a refined hybrid recommender approach based on Widrow-Hoff learning algorithm. The proposed approach employs Widrow-Hoff algorithm to learn each user?s profile from the contents of rated items, to improve the granularity of the user profiling. With the refined user profiles, collaborative filtering is employed to compute more precise similarity of different users, and predicts the ratings for unrated items. The improvement of performance is demonstrated by the experimental evaluation."}]