Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work  target personalized news and local event recommendations for their utility and societal importance.  evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition,  conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model shod significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations.
['Cheng-Kang Hsieh', 'Longqi Yang', 'Honghao Wei', 'Mor Naaman', 'Deborah Estrin']
I am writing a research paper on a idea Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces
[{"from": "human", "value": "I am writing a research paper on a idea Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work  target personalized news and local event recommendations for their utility and societal importance.  evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition,  conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model shod significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations."}]