Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences
This work addresses the sequentiality feature often overlooked in recommender systems, offering a novel approach for applications like music or playlist recommendations, though it appears incremental as it adapts existing NLP techniques.
The paper tackles the problem of recommending lists of multi-item sequences in recommender systems, moving beyond traditional next-item recommendations, and shows that using FastText for sequence modeling helps address cold-start user issues.
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.