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CS298 ProposalAI Dining Suggestion AppBao Pham (bao.t.pham@sjsu.edu) Advisor: Dr. Chris Pollett Committee Members: Dr. Mike Wu & Dr. Kevin Montgomery Abstract:Trying to decide what to eat sometimes can be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as APIs for using them. These restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an AI model that can learn from one's dining pattern over time to help make restaurant suggestions (with maybe some interesting options) at any time. CS297 Results
Proposed Schedule
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Innovations and Challenges
References:1. Pham, et al. "Device Placement Optimization with Reinforcement Learning." [Astro-Ph/0005112] A Determination of the Hubble Constant from Cepheid Distances and a Model of the Local Peculiar Velocity Field, American Physical Society, 25 June 2017, arxiv.org/abs/1706.04972. 2. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press. 3. Covington, Paul, et al. "Deep Neural Networks for YouTube Recommendations - Google AI." Google AI, 1 Jan. 1970, ai.google/research/pubs/pub45530. 4. Hasselt, et al. Deep Reinforcement Learning with Double Q-Learning. arxiv.org/pdf/1509.06461.pdf. 5. Burda, et al. "Large-Scale Study of Curiosity-Driven Learning." Curiosity-Driven Exploration by Self-Supervised Prediction, pathak22.github.io/large-scale-curiosity/. 6. https://developers.google.com/places/web-service/intro 7. https://www.yelp.com/developers/documentation/v3/business |