Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. 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. Recurrent neural network (RNN) has proved to be very effective for sequential modeling, and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario.
Through careful analysis of real-world datasets, we find that personalized item frequency (PIF) information (which records the number of times that each item is purchased by a user) provides two critical signals for NBR. But, this has been largely ignored by existing methods. Even though existing methods such as RNN based methods have strong representation ability, our empirical results show that they fail to learn and capture PIF. As a result, existing methods cannot fully exploit the critical signals contained in PIF. Given this inherent limitation of RNNs, we propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these critical signals. We evaluate our method on four public real-world datasets. Despite its relative simplicity, our method frequently outperforms the state-of-the-art NBR methods - including deep learning based methods using RNNs - when patterns associated with PIF play an important role in the data.
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Association Pattern Discovery Via Theme Dictionary Models
Discovering patterns from a set of text or, more generally, categorical data is an important problem in many disciplines such as biomedical research, linguistics, artificial intelligence and sociology. We consider here the well-known ‘market basket’ problem that is often discussed in the data mining community, and is also quite ubiquitous in biomedical research. The data under consideration are a set of ‘baskets’, where each basket contains a list of ‘items’. Our goal is to discover ‘themes’, which are defined as subsets of items that tend to co-occur in a basket. We describe a generative model, i.e. the theme dictionary model, for such data structures and describe two likelihood-based methods to infer themes that are hidden in a collection of baskets. We also propose a novel sequential Monte Carlo method to overcome computational challenges. Using both simulation studies and real applications, we demonstrate that the new approach proposed is significantly more powerful than existing methods, such as association rule mining and topic modelling, in detecting weak and subtle interactions in the data.
more » « less- PAR ID:
- 10401340
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Volume:
- 76
- Issue:
- 2
- ISSN:
- 1369-7412
- Format(s):
- Medium: X Size: p. 319-347
- Size(s):
- p. 319-347
- Sponsoring Org:
- National Science Foundation
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Abstract Living systems use chemical fuels to transiently assemble functional structures. As a step toward constructing abiotic mimics of such structures, we herein describe dissipative formation of covalent basket cage CBC
5 by reversible imine condensation of cup‐shaped aldehyde2 (i.e., basket) with trivalent aromatic amine4 . This nanosized [4+4] cage (V =5 nm3,M w=6150 Da) has shape of a truncated tetrahedron with four baskets at its vertices and four aromatic amines forming the faces. Importantly,tris ‐aldehyde basket2 and aliphatictris ‐amine7 undergo condensation to give small [1+1] cage6 . The imine metathesis of6 and aromatictris ‐amine4 into CBC5 was optimized to bias the equilibrium favouring6 . Addition of tribromoacetic acid (TBA) as a chemical fuel perturbs this equilibrium to result in the transient formation of CBC5 , with subsequent consumption of TBA via decarboxylation driving the system back to the starting state.
