Ajinkya Kale

Query Segmentation is one of the critical components for understanding users' search intent in Information Retrieval tasks. It involves grouping tokens in the search query into meaningful phrases which help downstream tasks like search relevance and query understanding. In this paper, we propose a novel approach to segment user queries using distributed query embeddings. Our key contribution is a supervised approach to the segmentation task using low-dimensional feature vectors for queries… Show more

Query Segmentation is one of the critical components for understanding users' search intent in Information Retrieval tasks. It involves grouping tokens in the search query into meaningful phrases which help downstream tasks like search relevance and query understanding. In this paper, we propose a novel approach to segment user queries using distributed query embeddings. Our key contribution is a supervised approach to the segmentation task using low-dimensional feature vectors for queries, getting rid of traditional hand tuned and heuristic NLP features which are quite expensive.
We benchmark on a 50,000 human-annotated web search engine query corpus achieving comparable accuracy to state-of-the-art techniques. The advantage of our technique is its fast and does not use external knowledge-base like Wikipedia for score boosting. This helps us generalize our approach to other domains like eCommerce without any fine-tuning. We demonstrate the effectiveness of this method on another 50,000 human-annotated eCommerce query corpus from eBay search logs. Our approach is easy to implement and generalizes well across different search domains proving the power of low-dimensional embeddings in query segmentation task, opening up a new direction of research for this problem. Show less

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to… Show more

In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought. Show less

We are in a phase of ‘Participatory Web’ in which users
‘add value’ to the information on the web by publishing,
tagging and sharing. The Participatory Web has enormous
potential for an enterprise because unlike the users of the
internet an enterprise is a community that shares common
goals, assumptions, vocabulary and interest and has reliable user identification and mutual trust along with a central governance and incentives to collaborate. Everyday, the
employees of an… Show more

We are in a phase of ‘Participatory Web’ in which users
‘add value’ to the information on the web by publishing,
tagging and sharing. The Participatory Web has enormous
potential for an enterprise because unlike the users of the
internet an enterprise is a community that shares common
goals, assumptions, vocabulary and interest and has reliable user identification and mutual trust along with a central governance and incentives to collaborate. Everyday, the
employees of an organization locate content relevant to their
work on the web. Finding this information takes time, expertise and creativity, which costs an organization money.
That is, the web pages employees find are knowledge assets owned by the enterprise. This investment in web-based
knowledge assets is lost every time the enterprise fails to
capture and reuse them. iCollaborate is tooled to capture
user’s web interaction, persist and analyze it, and feed that
interaction back into the community - the enterprise. Show less