Multi-Objective Personalized Product Retrieval in Taobao Search
- URL: http://arxiv.org/abs/2210.04170v1
- Date: Sun, 9 Oct 2022 05:18:42 GMT
- Title: Multi-Objective Personalized Product Retrieval in Taobao Search
- Authors: Yukun Zheng, Jiang Bian, Guanghao Meng, Chao Zhang, Honggang Wang,
Zhixuan Zhang, Sen Li, Tao Zhuang, Qingwen Liu, and Xiaoyi Zeng
- Abstract summary: We propose a novel Multi-Objective Personalized Product Retrieval (MOPPR) model with four hierarchical optimization objectives: relevance, exposure, click and purchase.
MOPPR achieves 0.96% transaction and 1.29% GMV improvements in a 28-day online A/B test.
Since the Double-11 shopping festival of 2021, MOPPR has been fully deployed in mobile Taobao search, replacing the previous MGDSPR.
- Score: 27.994166796745496
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In large-scale e-commerce platforms like Taobao, it is a big challenge to
retrieve products that satisfy users from billions of candidates. This has been
a common concern of academia and industry. Recently, plenty of works in this
domain have achieved significant improvements by enhancing embedding-based
retrieval (EBR) methods, including the Multi-Grained Deep Semantic Product
Retrieval (MGDSPR) model [16] in Taobao search engine. However, we find that
MGDSPR still has problems of poor relevance and weak personalization compared
to other retrieval methods in our online system, such as lexical matching and
collaborative filtering. These problems promote us to further strengthen the
capabilities of our EBR model in both relevance estimation and personalized
retrieval. In this paper, we propose a novel Multi-Objective Personalized
Product Retrieval (MOPPR) model with four hierarchical optimization objectives:
relevance, exposure, click and purchase. We construct entire-space
multi-positive samples to train MOPPR, rather than the single-positive samples
for existing EBR models.We adopt a modified softmax loss for optimizing
multiple objectives. Results of extensive offline and online experiments show
that MOPPR outperforms the baseline MGDSPR on evaluation metrics of relevance
estimation and personalized retrieval. MOPPR achieves 0.96% transaction and
1.29% GMV improvements in a 28-day online A/B test. Since the Double-11
shopping festival of 2021, MOPPR has been fully deployed in mobile Taobao
search, replacing the previous MGDSPR. Finally, we discuss several advanced
topics of our deeper explorations on multi-objective retrieval and ranking to
contribute to the community.
Related papers
- Large Language Models for Relevance Judgment in Product Search [48.56992980315751]
High relevance of retrieved and re-ranked items to the search query is the cornerstone of successful product search.
We present an array of techniques for leveraging Large Language Models (LLMs) for automating the relevance judgment of query-item pairs (QIPs) at scale.
Our findings have immediate implications for the growing field of relevance judgment automation in product search.
arXiv Detail & Related papers (2024-06-01T00:52:41Z) - Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning [55.96599486604344]
We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process.
We use Monte Carlo Tree Search (MCTS) to iteratively collect preference data, utilizing its look-ahead ability to break down instance-level rewards into more granular step-level signals.
The proposed algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this newly generated step-level preference data.
arXiv Detail & Related papers (2024-05-01T11:10:24Z) - Unified Embedding Based Personalized Retrieval in Etsy Search [0.42056926734482064]
We propose learning a unified embedding model incorporating graph, transformer and term-based embeddings end to end.
Our personalized retrieval model significantly improves the overall search experience, as measured by a 5.58% increase in search purchase rate and a 2.63% increase in site-wide conversion rate.
arXiv Detail & Related papers (2023-06-07T23:24:50Z) - How Does Generative Retrieval Scale to Millions of Passages? [68.98628807288972]
We conduct the first empirical study of generative retrieval techniques across various corpus scales.
We scale generative retrieval to millions of passages with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters.
While generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge.
arXiv Detail & Related papers (2023-05-19T17:33:38Z) - Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products
at Facebook Marketplace [15.054431410052851]
We present Que2Engage, a search EBR system built towards bridging the gap between retrieval and ranking for end-to-end optimizations.
We show the effectiveness of our approach via a multitask evaluation framework and thorough baseline comparisons and ablation studies.
arXiv Detail & Related papers (2023-02-21T23:10:16Z) - Multi-Task Fusion via Reinforcement Learning for Long-Term User
Satisfaction in Recommender Systems [3.4394890850129007]
We propose a Batch Reinforcement Learning based Multi-Task Fusion framework (BatchRL-MTF)
We learn an optimal recommendation policy from the fixed batch data offline for long-term user satisfaction.
With a comprehensive investigation on user behaviors, we model the user satisfaction reward with subtles from two aspects of user stickiness and user activeness.
arXiv Detail & Related papers (2022-08-09T06:35:05Z) - Multi-Label Learning to Rank through Multi-Objective Optimization [9.099663022952496]
Learning to Rank technique is ubiquitous in the Information Retrieval system nowadays.
To resolve ambiguity, it is desirable to train a model using many relevance criteria.
We propose a general framework where the information from labels can be combined in a variety of ways to characterize the trade-off among the goals.
arXiv Detail & Related papers (2022-07-07T03:02:11Z) - Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product
Retrieval [152.3504607706575]
This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.
We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks.
We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
arXiv Detail & Related papers (2022-06-17T15:40:45Z) - ItemSage: Learning Product Embeddings for Shopping Recommendations at
Pinterest [60.841761065439414]
At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases.
This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
arXiv Detail & Related papers (2022-05-24T02:28:58Z) - Product1M: Towards Weakly Supervised Instance-Level Product Retrieval
via Cross-modal Pretraining [108.86502855439774]
We investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval.
We contribute Product1M, one of the largest multi-modal cosmetic datasets for real-world instance-level retrieval.
We propose a novel model named Cross-modal contrAstive Product Transformer for instance-level prodUct REtrieval (CAPTURE)
arXiv Detail & Related papers (2021-07-30T12:11:24Z) - Generator and Critic: A Deep Reinforcement Learning Approach for Slate
Re-ranking in E-commerce [17.712394984304336]
We present a novel Generator and Critic slate re-ranking approach, where the Critic evaluates the slate and the Generator ranks the items by the reinforcement learning approach.
For the Generator, to tackle the problem of large action space, we propose a new exploration reinforcement learning algorithm, called PPO-Exploration.
arXiv Detail & Related papers (2020-05-25T16:24:01Z)
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.