Given a short query with keyphrases about a research idea in 2017 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A new model for recommendation systems that is able to capture user experience progression over time, her interest

Keyphrases: individual recommendation text rating

Dataset description:
TweetQA: With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effectiveness of many applications that rely on real-time knowledge. While previous question answering (QA) datasets have concentrated on formal text like news and Wikipedia, the first large-scale dataset for QA over social media data is presented.
BeerAdvocate: BeerAdvocate is a dataset that consists of beer reviews from beeradvocate. The data span a period of more than 10 years, including all ~1.5 million reviews up to November 2011.
MovieLens: The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time.
Frames Dataset: This dataset is dialog dataset collected in a Wizard-of-Oz fashion. Two humans talked to each other via a chat interface.

Options:- A: TweetQA  B: BeerAdvocate  C: MovieLens  D: Frames Dataset
BeerAdvocate