On Segmentation of eCommerce Queries

CIKM ’13 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management Pages 1137-1146
On Segmentation of eCommerce Queries
Nish Parikh, Prasad Sriram, Mohammad AlHasan

In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers′ data and also frequency data from product titles what we call sellers′ data.

We exploit the taxonomical structure of the marketplace to build domain specific frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation.

Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.

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Chaitali Gupta, Mayank Bansal, Tzu-Cheng Chuang, Ranjan Sinha, Sami Ben-romdhane

The sheer growth in data volume and Hadoop cluster size make it a significant challenge to diagnose and locate problems in a production-level cluster environment efficiently and within a short period of time. Often times, the distributed monitoring systems are not capable of detecting a problem well in advance when a large-scale Hadoop cluster starts to deteriorate i n performance or becomes unavailable. Thus, inc o m i n g workloads, scheduled between the time when cluster starts to deteriorate and the time when the problem is identified, suffer from longer execution times. As a result, both reliability and throughput of the cluster reduce significantly. In this paper, we address this problem by proposing a system called Astro, which consists of a predictive model and an extension to the Hadoop scheduler. The predictive model in Astro takes into account a rich set of cluster behavioral information that are collected by monitoring processes and model them using machine learning algorithms to predict future behavior of the cluster. The Astro predictive model detects anomalies in the cluster and also identifies a ranked set of metrics that have contributed the most towards the problem. The Astro scheduler uses the prediction outcome and the list of metrics to decide whether it needs to move and reduce workloads from the problematic cluster nodes or to prevent additional workload allocations to them, in order to improve both throughput and reliability of the cluster. The results demonstrate that the Astro scheduler improves usage of cluster compute resources significantly by 64.23% compared to traditional Hadoop. Furthermore, the runtime of the benchmark application reduced by 26.68% during the time of anomaly, thus improving the cluster throughput.