85 research outputs found
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
Personalized Category Frequency prediction for Buy It Again recommendations
Buy It Again (BIA) recommendations are crucial to retailers to help improve
user experience and site engagement by suggesting items that customers are
likely to buy again based on their own repeat purchasing patterns. Most
existing BIA studies analyze guests personalized behavior at item granularity.
A category-based model may be more appropriate in such scenarios. We propose a
recommendation system called a hierarchical PCIC model that consists of a
personalized category model (PC model) and a personalized item model within
categories (IC model). PC model generates a personalized list of categories
that customers are likely to purchase again. IC model ranks items within
categories that guests are likely to consume within a category. The
hierarchical PCIC model captures the general consumption rate of products using
survival models. Trends in consumption are captured using time series models.
Features derived from these models are used in training a category-grained
neural network. We compare PCIC to twelve existing baselines on four standard
open datasets. PCIC improves NDCG up to 16 percent while improving recall by
around 2 percent. We were able to scale and train (over 8 hours) PCIC on a
large dataset of 100M guests and 3M items where repeat categories of a guest
out number repeat items. PCIC was deployed and AB tested on the site of a major
retailer, leading to significant gains in guest engagement.Comment: This work appears as a short paper in RecSys 202
A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
Recommendation systems have wide-spread applications in both academia and
industry. Traditionally, performance of recommendation systems has been
measured by their precision. By introducing novelty and diversity as key
qualities in recommender systems, recently increasing attention has been
focused on this topic. Precision and novelty of recommendation are not in the
same direction, and practical systems should make a trade-off between these two
quantities. Thus, it is an important feature of a recommender system to make it
possible to adjust diversity and accuracy of the recommendations by tuning the
model. In this paper, we introduce a probabilistic structure to resolve the
diversity-accuracy dilemma in recommender systems. We propose a hybrid model
with adjustable level of diversity and precision such that one can perform this
by tuning a single parameter. The proposed recommendation model consists of two
models: one for maximization of the accuracy and the other one for
specification of the recommendation list to tastes of users. Our experiments on
two real datasets show the functionality of the model in resolving
accuracy-diversity dilemma and outperformance of the model over other classic
models. The proposed method could be extensively applied to real commercial
systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure
Sequential recommender systems: Challenges, progress and prospects
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users' preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area
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