Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences

TL;DR

This paper introduces a sequential recommendation approach using FastText embeddings to recommend lists of item sequences, improving cold-start issues and evaluation methods by adapting NLP metrics like ROUGE.

Contribution

It proposes a novel sequential recommendation method leveraging FastText for modeling item sequences and evaluates recommendations with NLP-inspired metrics.

Findings

Sequence-based recommendations outperform traditional methods.

FastText embeddings help mitigate cold-start problems.

ROUGE metric effectively evaluates sequence recommendations.

Abstract

Recommender systems (RS) suggest items-based on the estimated preferences of users. Recent RS methods utilise vector space embeddings and deep learning methods to make efficient recommendations. However, most of these methods overlook the sequentiality feature and consider each interaction, e.g., check-in, independent from each other. The proposed method considers the sequentiality of the interactions of users with items and uses them to make recommendations of a list of multi-item sequences. The proposed method uses FastText \cite{bojanowski2016enriching}, a well-known technique in natural language processing (NLP), to model the relationship among the subunits of sequences, e.g., tracks, playlists, and utilises the trained representation as an input to a traditional recommendation method. The recommended lists of multi-item sequences are evaluated by the ROUGE \cite{lin2003automatic,lin2004rouge} metric, which is also commonly used in the NLP literature. The current experimental results reveal that it is possible to recommend a list of multi-item sequences, in addition to the traditional next item recommendation. Also, the usage of FastText, which utilise sub-units of the input sequences, helps to overcome cold-start user problem.