Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling the evolution of users' preference over time is essential for personalized recommendation. Traditional time-aware models like (1) time-window or recency based approaches ignore or deemphasize much potentially useful information, and (2) time-aware collaborative filtering (CF) approaches largely rely on the information of other users, thus failing to precisely and comprehensively profile individual users for personalization. , for implicit feedback data,  propose a personalized recommendation model to capture users' dynamic preference using Gaussian process.  first apply topic modeling to represent a user's temporal preference in an interaction as a topic distribution. By aggregating such topic distributions of the user's past interactions,  build her profile, where  treat each topic's values at different interactions as a time series. Gaussian process is then applied to predict the user's preference in the next interactions for top-N recommendation. Experiments conducted over two real datasets demonstrate that our approach outperforms the state-of-the-art recommendation models by at least 42:46% and 66:14% in terms of precision and Mean Reciprocal Rank respectively.
['Xin Liu']
I am writing a research paper on a idea Modeling users' dynamic preference for personalized recommendation
[{"from": "human", "value": "I am writing a research paper on a idea Modeling users' dynamic preference for personalized recommendation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Modeling the evolution of users' preference over time is essential for personalized recommendation. Traditional time-aware models like (1) time-window or recency based approaches ignore or deemphasize much potentially useful information, and (2) time-aware collaborative filtering (CF) approaches largely rely on the information of other users, thus failing to precisely and comprehensively profile individual users for personalization. , for implicit feedback data,  propose a personalized recommendation model to capture users' dynamic preference using Gaussian process.  first apply topic modeling to represent a user's temporal preference in an interaction as a topic distribution. By aggregating such topic distributions of the user's past interactions,  build her profile, where  treat each topic's values at different interactions as a time series. Gaussian process is then applied to predict the user's preference in the next interactions for top-N recommendation. Experiments conducted over two real datasets demonstrate that our approach outperforms the state-of-the-art recommendation models by at least 42:46% and 66:14% in terms of precision and Mean Reciprocal Rank respectively."}]