Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Internet user behavior models characterize user browsing dynamics or the transitions among b pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific b pages can be customized and prefetched for individuals based on sequences of b pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time.  realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure.  demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths.
['Xiaokui Shu', 'Nikolay Laptev', 'Danfeng Daphne Yao']
I am writing a research paper on a idea DECT: distributed evolving context tree for understanding user behavior pattern evolution
[{"from": "human", "value": "I am writing a research paper on a idea DECT: distributed evolving context tree for understanding user behavior pattern evolution"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Internet user behavior models characterize user browsing dynamics or the transitions among b pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific b pages can be customized and prefetched for individuals based on sequences of b pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time.  realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure.  demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths."}]