ترغب بنشر مسار تعليمي؟ اضغط هنا

Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning

121   0   0.0 ( 0 )
 نشر من قبل Ryuichi Takanobu
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment. First, unlike traditional aggregated web search that merely presents multi-sourced results in the first page, this new task may present aggregated results in all pages and has to dynamically decide which source should be presented in the current page. Second, as pointed out by many existing studies, it is not trivial to rank items from heterogeneous sources because the relevance scores from different source systems are not directly comparable. To address these two issues, we decompose the task into two subtasks in a hierarchical structure: a high-level task for source selection where we model the sequential patterns of user behaviors onto aggregated results in different pages so as to understand user intents and select the relevant sources properly; and a low-level task for item presentation where we formulate a slot filling process to sequentially present the items instead of giving each item a relevance score when deciding the presentation order of heterogeneous items. Since both subtasks can be naturally formulated as sequential decision problems and learn from the future user feedback on search results, we build our model with hierarchical reinforcement learning. Extensive experiments demonstrate that our model obtains remarkable improvements in search performance metrics, and achieves a higher user satisfaction.



قيم البحث

اقرأ أيضاً

We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant i tems to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.
Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnessed a flurry of research on the use of Transformer-style models and de ep text-match models to improve relevance. However, these two types of models ignored the inherent bipartite network structures that are ubiquitous in e-commerce search logs, making these models ineffective. We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction. We design, for the first time, an end-to-end First-and-Second-order Relevance prediction model for e-commerce item relevance. The model is augmented by the neighborhood structures of bipartite networks that are built using the information of user behavioral feedback, including clicks and purchases. To ensure that edges accurately encode relevance information, we introduce external knowledge generated from BERT to refine the network of user behaviors. This allows the new model to integrate information from neighboring items and queries, which are highly relevant to the focus query-item pair under consideration. Results of offline experiments showed that the new model significantly improved the prediction accuracy in terms of human relevance judgment. An ablation study showed that the First-and-Second-order model gained a 4.3% average gain over the First-order model. Results of an online A/B test revealed that the new model derived more commercial benefits compared to the base model.
Nowadays e-commerce search has become an integral part of many peoples shopping routines. One critical challenge in todays e-commerce search is the semantic matching problem where the relevant items may not contain the exact terms in the user query. In this paper, we propose a novel deep neural network based approach to query rewriting, in order to tackle this problem. Specifically, we formulate query rewriting into a cyclic machine translation problem to leverage abundant click log data. Then we introduce a novel cyclic consistent training algorithm in conjunction with state-of-the-art machine translation models to achieve the optimal performance in terms of query rewriting accuracy. In order to make it practical in industrial scenarios, we optimize the syntax tree construction to reduce computational cost and online serving latency. Offline experiments show that the proposed method is able to rewrite hard user queries into more standard queries that are more appropriate for the inverted index to retrieve. Comparing with human curated rule-based method, the proposed model significantly improves query rewriting diversity while maintaining good relevancy. Online A/B experiments show that it improves core e-commerce business metrics significantly. Since the summer of 2020, the proposed model has been launched into our search engine production, serving hundreds of millions of users.
Typical e-commerce platforms contain millions of products in the catalog. Users visit these platforms and enter search queries to retrieve their desired products. Therefore, showing the relevant products at the top is essential for the success of e-c ommerce platforms. We approach this problem by learning low dimension representations for queries and product descriptions by leveraging user click-stream data as our main source of signal for product relevance. Starting from GRU-based architectures as our baseline model, we move towards a more advanced transformer-based architecture. This helps the model to learn contextual representations of queries and products to serve better search results and understand the user intent in an efficient manner. We perform experiments related to pre-training of the Transformer based RoBERTa model using a fashion corpus and fine-tuning it over the triplet loss. Our experiments on the product ranking task show that the RoBERTa model is able to give an improvement of 7.8% in Mean Reciprocal Rank(MRR), 15.8% in Mean Average Precision(MAP) and 8.8% in Normalized Discounted Cumulative Gain(NDCG), thus outperforming our GRU based baselines. For the product retrieval task, RoBERTa model is able to outperform other two models with an improvement of 164.7% in Precision@50 and 145.3% in Recall@50. In order to highlight the importance of pre-training RoBERTa for fashion domain, we qualitatively compare already pre-trained RoBERTa on standard datasets with our custom pre-trained RoBERTa over a fashion corpus for the query token prediction task. Finally, we also show a qualitative comparison between GRU and RoBERTa results for product retrieval task for some test queries.
With the rapid growth of e-Commerce, online product search has emerged as a popular and effective paradigm for customers to find desired products and engage in online shopping. However, there is still a big gap between the products that customers rea lly desire to purchase and relevance of products that are suggested in response to a query from the customer. In this paper, we propose a robust way of predicting relevance scores given a search query and a product, using techniques involving machine learning, natural language processing and information retrieval. We compare conventional information retrieval models such as BM25 and Indri with deep learning models such as word2vec, sentence2vec and paragraph2vec. We share some of our insights and findings from our experiments.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا