Many-to-one Recurrent Neural Network for Session-based Recommendation

TL;DR

This paper introduces a many-to-one RNN model combining rule-based methods for session-based accommodation recommendation, showing promising results but with high computational costs and tuning requirements.

Contribution

It presents a novel RNN-based approach for session-based recommendation that integrates rule-based algorithms, optimized through hyper-parameter tuning.

Findings

The model predicts user clicks effectively based on session sequences.

Combining rule-based and RNN methods improves recommendation quality.

Training is computationally intensive and requires careful tuning.

Abstract

This paper presents the D2KLab team's approach to the RecSys Challenge 2019 which focuses on the task of recommending accommodations based on user sessions. What is the feeling of a person who says "Rooms of the hotel are enormous, staff are friendly and efficient"? It is positive. Similarly to the sequence of words in a sentence where one can affirm what the feeling is, analysing a sequence of actions performed by a user in a website can lead to predict what will be the item the user will add to his basket at the end of the shopping session. We propose to use a many-to-one recurrent neural network that learns the probability that a user will click on an accommodation based on the sequence of actions he has performed during his browsing session. More specifically, we combine a rule-based algorithm with a Gated Recurrent Unit RNN in order to sort the list of accommodations that is shown to the user. We optimized the RNN on a validation set, tuning the hyper-parameters such as the learning rate, the batch-size and the accommodation embedding size. This analogy with the sentiment analysis task gives promising results. However, it is computationally demanding in the training phase and it needs to be further tuned.