Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Historical user activity is key for building user profiles to predict the user behavior and affinities in many b applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related b pages may ll suggest that the user might be shopping for a new vehicle.  present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allod to vary over time, thus ensuring that the profiles remain current.    describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.
['Amr Ahmed', 'Yucheng Low', 'Mohamed Aly', 'Vanja Josifovski', 'Alexander J. Smola']
I am writing a research paper on a idea Scalable distributed inference of dynamic user interests for behavioral targeting
[{"from": "human", "value": "I am writing a research paper on a idea Scalable distributed inference of dynamic user interests for behavioral targeting"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Historical user activity is key for building user profiles to predict the user behavior and affinities in many b applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related b pages may ll suggest that the user might be shopping for a new vehicle.  present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \\emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allod to vary over time, thus ensuring that the profiles remain current.    describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers."}]