Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender systems have been widely used in our daily life to recommend objects to users meeting the users' preference. ,  focus on objects with temporally variable features such as restaurant with seasonal dishes and point-of-interests (POIs) to have seasonal attractions, and propose a method to automatically generate temporal feature vectors for those objects. The basic idea of the proposed method is: 1) to identify the vocabulary concerned with objects through Wikipedia; 2) to identify the trend over all objects through Twitter; and 3) to highlight the ight of words contained in each identified trend to obtain temporal feature vectors for each object.  built a tourism recommender system to evaluate the effectiveness of the proposed method. The result of experiments indicates that: 1) the variance of temporal feature vectors follows the Gaussian distribution, 2) those vectors certainly reflect the similarity of POIs for a designated time period, and 3) such a property of feature vectors can be effectively used for the seasonal recommendation of POIs.
['Guanshen Fang', 'Sayaka Kamei', 'Satoshi Fujita']
I am writing a research paper on a idea Automatic Generation of Temporal Feature Vectors with Application to Tourism Recommender Systems
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Generation of Temporal Feature Vectors with Application to Tourism Recommender Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recommender systems have been widely used in our daily life to recommend objects to users meeting the users' preference. ,  focus on objects with temporally variable features such as restaurant with seasonal dishes and point-of-interests (POIs) to have seasonal attractions, and propose a method to automatically generate temporal feature vectors for those objects. The basic idea of the proposed method is: 1) to identify the vocabulary concerned with objects through Wikipedia; 2) to identify the trend over all objects through Twitter; and 3) to highlight the ight of words contained in each identified trend to obtain temporal feature vectors for each object.  built a tourism recommender system to evaluate the effectiveness of the proposed method. The result of experiments indicates that: 1) the variance of temporal feature vectors follows the Gaussian distribution, 2) those vectors certainly reflect the similarity of POIs for a designated time period, and 3) such a property of feature vectors can be effectively used for the seasonal recommendation of POIs."}]