A Text-Based Recommender System that Leverages Explicit Affective State Preferences

TL;DR

This paper introduces a novel affective state-based recommendation system that uses a large dataset of fine-grained emotional preferences from reviews and a Transformer architecture to improve personalized recommendations.

Contribution

It presents a new recommendation task leveraging explicit affective states, a large dataset of affective preferences, and a Transformer-based model to enhance recommendation accuracy.

Findings

Models utilizing textual descriptions and affective preferences perform best.

A large dataset of affective states from reviews was created and used.

Transformer-based models effectively leverage affective expressions for recommendations.

Abstract

The affective attitude of liking a recommended item reflects just one category in a wide spectrum of affective phenomena that also includes emotions such as entranced or intrigued, moods such as cheerful or buoyant, as well as more fine-grained affective states, such as "pleasantly surprised by the conclusion". In this paper, we introduce a novel recommendation task that can leverage a virtually unbounded range of affective states sought explicitly by the user in order to identify items that, upon consumption, are likely to induce those affective states. Correspondingly, we create a large dataset of user preferences containing expressions of fine-grained affective states that are mined from book reviews, and propose a Transformer-based architecture that leverages such affective expressions as input. We then use the resulting dataset of affective states preferences, together with the linked users and their histories of book readings, ratings, and reviews, to train and evaluate multiple recommendation models on the task of matching recommended items with affective preferences. Experiments show that the best results are obtained by models that can utilize textual descriptions of items and user affective preferences.