Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work presents ai¾?Recommender System RS that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion explicit data and navigating through the b-page implicit data. The Matrix Factorization MF recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in ani¾?independent layer. Indeed, using past preferences,  deeply analyze the implicit interest of users in the attributes of items. By using this,  transform ratings and predictions into "semantic values", where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database.  compare our work against ai¾?simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be parallelized, alleviating time processing in large amount of data.
['Manuel Pozo', 'Raja Chiky', 'Elisabeth Métais']
I am writing a research paper on a idea Enhancing Collaborative Filtering Using Implicit Relations in Data
[{"from": "human", "value": "I am writing a research paper on a idea Enhancing Collaborative Filtering Using Implicit Relations in Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work presents ai\u00be?Recommender System RS that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion explicit data and navigating through the b-page implicit data. The Matrix Factorization MF recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in ani\u00be?independent layer. Indeed, using past preferences,  deeply analyze the implicit interest of users in the attributes of items. By using this,  transform ratings and predictions into \"semantic values\", where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database.  compare our work against ai\u00be?simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be parallelized, alleviating time processing in large amount of data."}]