570 research outputs found
Attribute-aware Diversification for Sequential Recommendations
Users prefer diverse recommendations over homogeneous ones. However, most
previous work on Sequential Recommenders does not consider diversity, and
strives for maximum accuracy, resulting in homogeneous recommendations. In this
paper, we consider both accuracy and diversity by presenting an Attribute-aware
Diversifying Sequential Recommender (ADSR). Specifically, ADSR utilizes
available attribute information when modeling a user's sequential behavior to
simultaneously learn the user's most likely item to interact with, and their
preference of attributes. Then, ADSR diversifies the recommended items based on
the predicted preference for certain attributes. Experiments on two benchmark
datasets demonstrate that ADSR can effectively provide diverse recommendations
while maintaining accuracy.Comment: AIIS 2020, as part of SIGIR 2020 https://aiis.newidea.fun
Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
Next basket recommendation (NBR) is the task of predicting the next set of
items based on a sequence of already purchased baskets. It is a recommendation
task that has been widely studied, especially in the context of 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. Most NBR work either
ignores this distinction or focuses on repeat items. We formulate the next
novel basket recommendation (NNBR) task, i.e., the task of recommending a
basket that only consists of novel items, which is valuable for both real-world
application and NBR evaluation. We evaluate how existing NBR methods perform on
the NNBR task and find that, so far, limited progress has been made w.r.t. the
NNBR task. To address the NNBR task, we propose a simple bi-directional
transformer basket recommendation model (BTBR), which is focused on directly
modeling item-to-item correlations within and across baskets instead of
learning complex basket representations. To properly train BTBR, we propose and
investigate several masking strategies and training objectives: (i) item-level
random masking, (ii) item-level select masking, (iii) basket-level all masking,
(iv) basket-level explore masking, and (v) joint masking. In addition, an
item-basket swapping strategy is proposed to enrich the item interactions
within the same baskets. We conduct extensive experiments on three open
datasets with various characteristics. The results demonstrate the
effectiveness of BTBR and our masking and swapping strategies for the NNBR
task. BTBR with a properly selected masking and swapping strategy can
substantially improve NNBR performance.Comment: To appear at RecSys'2
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