Next-basket recommendation
8 papers with code • 2 benchmarks • 0 datasets
Most implemented papers
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
BoPeng112/M2
•
•
We compared M2 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.
Modeling Personalized Item Frequency Information for Next-basket Recommendation
HaojiHu/TIFUKNN
•
•
NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.
Correlation-Sensitive Next-Basket Recommendation
PreferredAI/beacon
•
•
The Twenty-Eighth International Joint Conference on Artificial Intelligence Conference 2019
Items adopted by a user over time are indicative of the underlying preferences.
A Next Basket Recommendation Reality Check
liming-7/a-next-basket-recommendation-reality-check
•
•
We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models.
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items
Our results show that our 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 we delete existing baskets.
Time-Aware Item Weighting for the Next Basket Recommendations
alexeyromanov-hse/time_aware_item_weighting
•
•
In addition, we show the results of an ablation study and a case study of a few items.
Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
In next basket recommendation (NBR), it is useful to distinguish between repeat items, i. e., items that a user has consumed before, and explore items, i. e., items that a user has not consumed before.
[RE] Modeling Personalized Item Frequency Information for Next-basket Recommendation
This paper focuses on reproducing and extending the results of the paper: "Modeling Personalized Item Frequency Information for Next-basket Recommendation" which introduced the TIFU-KNN model and proposed to utilize Personalized Item Frequency (PIF) for Next Basket Recommendation (NBR).
