Multi-Objective Recommender System- A need of the hour

Why I choose this topic:

This is in fact an old topic, which I worked a decade ago with a company called RichRelevance - the Personalization Leader. So, I was ignored this topic so far. Few days back, I came acrosss this kaggle competation by @kaggle. So, I started collecting more information on the subject in order to participate in that competation, which I summarized below.

Kaggle competation:

Welcome to the OTTO — RecSys Challenge 2022! say @kaggle Looking at the data set this is a Session-based recommendation, a sub-area of sequential recommendation system. This has been an important task in online services like e-commerce, where most users either browse in a session anonymously or may have very distinct interests for many different sessions. Session-Based Recommender Systems (SBRS) have been proposed to model the sequence of interactions within the current user session, where a session is a short sequence of user interactions typically bounded by user inactivity. They have recently gained popularity due to their ability to capture short-term or contextual user preferences towards items.

In this regard, the aim here is to present to the participants: (i) an introduction on the main concepts and algorithms for session-based recommendation, (ii) how to build, train and evaluate a session-based recommendation model based on RNN and Transformer architectures, and (iii) how to speed up with GPUs the entire RecSys pipeline which encompasses…