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miscellaneous-10

Next-basket recommendation

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

M2: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation

Xia Ning, B. Peng, Zhiyun Ren, Srinivas Parthasarathy•Thu Apr 02 2020

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.

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Modeling Personalized Item Frequency Information for Next-basket Recommendation

Xiangnan He, Haoji Hu, Jinyang Gao, Zhi-Li Zhang•Sat May 30 2020

A simple item frequency based k-nearest neighbors (kNN) method is proposed to directly utilize the critical signals contained in PIF and frequently outperforms the state-of-the-art NBR methods when patterns associated with PIF play an important role in the data.

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Correlation-Sensitive Next-Basket Recommendation

Duc-Trong Le, Hady W. Lauw, Yuan Fang•Wed Jul 31 2019

It is hypothesize that incorporating information on pairwise correlations among items would help to arrive at more coherent basket recommendations, and develops a hierarchical network architecture codenamed Beacon to model basket sequences.

87 0

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A Next Basket Recommendation Reality Check

M. de Rijke, Mozhdeh Ariannezhad, Ming Li, Sami Jullien•Tue Sep 28 2021

This work provides a novel angle on the evaluation of next basket recommendation (NBR) methods, centered on the distinction between repetition and exploration, and proposes a set of metrics that measure the repetition/exploration ratio and performance of NBR models.

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Time-Aware Item Weighting for the Next Basket Recommendations

A. Romanov, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov•Sat Jul 29 2023

A novel method, Time-Aware Item-based Weighting (TAIW), which takes timestamps and intervals into account and outperforms well-tuned state-of-the-art baselines for next-basket recommendations.

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Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items

Sebastian Schelter, Benjamin Longxiang Wang•Wed Jan 26 2022

The results show that the method provides constant update time efficiency with respect to an additional user basket in the incremental case, and linear efficiency in the decremental case where the authors delete existing baskets.

7 0

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Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping

M. de Rijke, Ming Li, Mozhdeh Ariannezhad, Andrew Yates•Tue Aug 01 2023

A simple bi-directional transformer basket recommendation model (BTBR) is proposed, which is focused on directly modeling item-to-item correlations within and across baskets instead of learning complex basket representations.

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[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation

Slawomir Garcarz, Avik Pal, Pim Praat•Mon Feb 26 2024

The experimental results confirmed that the reproduced model, TIFU-KNN, outperforms the baseline model, Personal Top Frequency, on various datasets and metrics.

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Next-basket recommendation | State-of-the-Art