Low-Rank Online Dynamic Assortment with Dual Contextual Information

• URL: http://arxiv.org/abs/2404.17592v1
• Date: Fri, 19 Apr 2024 23:10:12 GMT
• Title: Low-Rank Online Dynamic Assortment with Dual Contextual Information
• Authors: Seong Jin Lee, Will Wei Sun, Yufeng Liu,
• Abstract summary: We introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. We then propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making.
• Score: 12.373566593905792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making. Theoretically, we establish a regret bound of $\tilde{O}((d_1+d_2)r\sqrt{T})$, where $d_1, d_2$ represent the dimensions of the user and item features respectively, $r$ is the rank of the parameter matrix, and $T$ denotes the time horizon. This bound represents a substantial improvement over prior literature, made possible by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method.

Related papers

• Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator [60.07198935747619]
  We propose Twin-Tower Dynamic Semantic Recommender (T TDS), the first generative RS which adopts dynamic semantic index paradigm. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender. The proposed T TDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.
  arXiv  Detail & Related papers  (2024-09-14T01:45:04Z)
• Minimax and Communication-Efficient Distributed Best Subset Selection with Oracle Property [0.358439716487063]
  The explosion of large-scale data has outstripped the processing capabilities of single-machine systems. Traditional approaches to distributed inference often struggle with achieving true sparsity in high-dimensional datasets. We propose a novel, two-stage, distributed best subset selection algorithm to address these issues.
  arXiv  Detail & Related papers  (2024-08-30T13:22:08Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints [10.057344315478709]
  We study the contextual dynamic pricing problem where a firm sells products to $T$ sequentially arriving consumers. We first show that the optimal regret upper bound is of order $sqrtdT$, up to a logarithmic factor. A key insight of our theoretical result is an intrinsic connection between dynamic pricing and the contextual multi-armed bandit problem.
  arXiv  Detail & Related papers  (2024-06-04T15:44:10Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Online Learning under Budget and ROI Constraints via Weak Adaptivity [57.097119428915796]
  Existing primal-dual algorithms for constrained online learning problems rely on two fundamental assumptions. We show how such assumptions can be circumvented by endowing standard primal-dual templates with weakly adaptive regret minimizers. We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions.
  arXiv  Detail & Related papers  (2023-02-02T16:30:33Z)
• Online Allocation with Two-sided Resource Constraints [44.5635910908944]
  We consider an online allocation problem subject to lower and upper resource constraints, where the requests arrive sequentially. We propose a new algorithm that obtains $1-O(fracepsilonalpha-epsilon)$ -competitive ratio for the offline problems that know the entire requests ahead of time.
  arXiv  Detail & Related papers  (2021-12-28T02:21:06Z)
• Index $t$-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings [1.7188280334580195]
  This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The proposed algorithm has the same complexity as the original $t$-SNE to embed new items, and a lower one when considering the embedding of a dataset sliced into sub-pieces.
  arXiv  Detail & Related papers  (2021-09-22T06:45:37Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling [0.5156484100374059]
  We introduce a method involving multi-goal sampling followed by ranking for user-relevance (Sample-Rank) to nudge recommendations towards multi-objective goals of the marketplace. The proposed method's novelty is that it reduces the MO recommendation problem to sampling from a desired multi-goal distribution then using it to build a production-friendly learning-to-rank model.
  arXiv  Detail & Related papers  (2020-08-24T09:17:18Z)
• A Generic Network Compression Framework for Sequential Recommender Systems [71.81962915192022]
  Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. We propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed. By the extensive ablation studies, we demonstrate that the proposed CpRec can achieve up to 4$sim$8 times compression rates in real-world SRS datasets.
  arXiv  Detail & Related papers  (2020-04-21T08:40:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.