M2: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation (2020-04-03T00:00:00.000000Z)

TL;DR

A novel mixed model with preferences, popularities and transitions, which significantly outperforms the state-of-the-art next-basket recommendation methods on 4 public benchmark datasets.

Abstract

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (<inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {M^2}}\limits$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="monospace">M</mml:mi><mml:mn mathvariant="monospace">2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ning-ieq1-3142773.gif"/></alternatives></inline-formula>) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users’ general preferences, 2) items’ global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {M^2}}\limits$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="monospace">M</mml:mi><mml:mn mathvariant="monospace">2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ning-ieq2-3142773.gif"/></alternatives></inline-formula> does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (<inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {ed\text{-}Trans}}\limits$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="monospace">ed</mml:mi><mml:mtext mathvariant="monospace">-</mml:mtext><mml:mi mathvariant="monospace">Trans</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="ning-ieq3-3142773.gif"/></alternatives></inline-formula>) to better model the transition patterns among items. We compared <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {M^2}}\limits$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="monospace">M</mml:mi><mml:mn mathvariant="monospace">2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ning-ieq4-3142773.gif"/></alternatives></inline-formula> with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {M^2}}\limits$</tex-math><alternatives><mml:math><mml:msup><mml:mi mathvariant="monospace">M</mml:mi><mml:mn mathvariant="monospace">2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ning-ieq5-3142773.gif"/></alternatives></inline-formula> significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {ed\text{-}Trans}}\limits$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="monospace">ed</mml:mi><mml:mtext mathvariant="monospace">-</mml:mtext><mml:mi mathvariant="monospace">Trans</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="ning-ieq6-3142773.gif"/></alternatives></inline-formula> is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.

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Visualizing Data using t-SNE

MapReduce: simplified data processing on large clusters

interests include machine learning, data mining and their applications in learning analytics, recommender systems and biomedical informatics

Department of Computer Science, University of Rochester, Rochester, currently a Professor at the Computer Science and Department, and the Biomedical Informatics Department, University