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Data Mining and Knowledge Discovery

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12.03.2021

Sequential recommendation with metric models based on frequent sequences

verfasst von: Corentin Lonjarret, Roch Auburtin, Céline Robardet, Marc Plantevit

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 3/2021

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Abstract

Modeling user preferences (long-term history) and user dynamics (short-term history) is of greatest importance to build efficient sequential recommender systems. The challenge lies in the successful combination of the whole user’s history and his recent actions (sequential dynamics) to provide personalized recommendations. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains (usually first order chains) regardless of the user, which limits both the impact of the past of the user on the recommendation and the ability to adapt its length to the user profile. In this article, we propose to use frequent sequences to identify the most relevant part of the user history for the recommendation. The most salient items are then used in a unified metric model that embeds items based on user preferences and sequential dynamics. Extensive experiments demonstrate that our method outperforms state-of-the-art, especially on sparse datasets. We show that considering sequences of varying lengths improves the recommendations and we also emphasize that these sequences provide explanations on the recommendation.

Vorheriger Artikel Multi-label learning with missing and completely unobserved labels

Nächster Artikel Tackling ordinal regression problem for heterogeneous data: sparse and deep multi-task learning approaches

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https://bit.ly/3gwZAOF.

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Only for Foursquare dataset, we observed that it is better not to have \(\gamma \) (remove \(\gamma \) and \((1-\gamma )\) in Equation 3). It is noteworthy that recommendations may be different to the case where \(\gamma =0.5\) due to the bias terms \(\beta _i\).

Best hyperparameters for each dataset are reported in supplementary material (Lonjarret et al. 2020a).

We only show HIT_25, HIT_50, NDCG_25 and NDCG_50 in the tables. HIT_5, HIT_10, NDCG_5 and NDCG_10 are reported in supplementary material (Lonjarret et al. 2020a).

Performances of other nearest-neighbor-based approaches as Item-based KNN and Sequence-Aware Extensions (V-S-KNN, S-S-KNN and SF-S-KNN) are reported in supplementary material (Lonjarret et al. 2020a).

To avoid running again the grid search, we took the best combination of hyperparameters that we previously found for k = 10.

We only show HIT_25, HIT_50, NDCG_25 and NDCG_50 in the table. HIT_5, HIT_10, NDCG_5 and NDCG_10 are reported in supplementary material (Lonjarret et al. 2020a).

The two numbers after the name of the dataset are respectively the value of \({\texttt {minCount}}\) and L.

Column (A) and (B) are both equivalent to a first order Markov chain but we split it into two different columns to point out the fact that it is very uncommon that no item from F matches \(s_{u}^{[1,t]}\)

Sequences that do not match any substring of F are omitted for columns (F) and (G).

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Titel: Sequential recommendation with metric models based on frequent sequences
verfasst von: Corentin Lonjarret
Roch Auburtin
Céline Robardet
Marc Plantevit
Publikationsdatum: 12.03.2021
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 3/2021
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-021-00744-w

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