Personalized Bundle List Recommendation
| Autor: | Zhao Li, Weiting An, Junshuai Song, Jinze Bai, Xiaoru Qu, Jun Gao, Chang Zhou |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science media_common.quotation_subject 02 engineering and technology Competitor analysis 010501 environmental sciences computer.software_genre 01 natural sciences Computer Science - Information Retrieval Artificial Intelligence (cs.AI) Bundle 0202 electrical engineering electronic engineering information engineering Beam search 020201 artificial intelligence & image processing Quality (business) Data mining Representation (mathematics) Database transaction computer Information Retrieval (cs.IR) 0105 earth and related environmental sciences media_common |
| Zdroj: | WWW |
| Popis: | Product bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online e-commerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles boosts the user-experience and eventually increases transaction volume. In this paper, we formalize the personalized bundle list recommendation as a structured prediction problem and propose a bundle generation network (BGN), which decomposes the problem into quality/diversity parts by the determinantal point processes (DPPs). BGN uses a typical encoder-decoder framework with a proposed feature-aware softmax to alleviate the inadequate representation of traditional softmax, and integrates the masked beam search and DPP selection to produce high-quality and diversified bundle list with an appropriate bundle size. We conduct extensive experiments on three public datasets and one industrial dataset, including two generated from co-purchase records and the other two extracted from real-world online bundle services. BGN significantly outperforms the state-of-the-art methods in terms of quality, diversity and response time over all datasets. In particular, BGN improves the precision of the best competitors by 16\% on average while maintaining the highest diversity on four datasets, and yields a 3.85x improvement of response time over the best competitors in the bundle list recommendation problem. WWW2019, 11 pages |
| Databáze: | OpenAIRE |
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