Day 2 of #RecSys2022: Our favorite 5 papers and talks
It’s been another fantastic day at RecSys 2022. Following the Women in RecSys Breakfast, the day started with a keynote from Catherine D’Ignazio and then throughout the day had the following sessions: Fairness & Privacy, Diversity & Novely, and Models and Learning I. Here are our favorite 5 papers and talks.
The authors bring the power of large language models into the RecSys ecosystem. They present P5, a unified pretrain, personalized prompt & predict paradigm built on top of T5 checkpoints. It uses all data including user-item interactions, item metadata, and user reviews are converts them to a common format — natural language using prompt templates.
This new data formulation allow the authors to train P5 as a multi-task recommender where different 47 personalized prompts to cover 5 task families are used for training.
Each prompt consist of input–target pairs from raw data - simply substituting the fields in the prompts with the corresponding information. The raw data for the five task families of P5are from three separate sources. Specifically, rating/review/explanation prompts (a) have shared raw data. Sequential recommendation (b) and direct recommendation (c) uses similar raw data, but the former particularly requires the user interaction history.
