Workshop at WSDM-2014
Abstract [+]
Personalization is central to most Internet experiences. Personalization is a data-driven process, whether the data are explicitly gathered (e.g., by asking people to fill out forms) or implicitly (e.g. through analysis of behavioral data). It is clear that designing for effective personalization poses interesting engineering and computer science challenges. However, personalization is also a user experience issue. We believe that encouraging dialogue and collaboration between data mining experts, content providers, and user-focused researchers will offer gains in the area of personalization for search and for other domains. This workshop is part of a larger effort we are developing: D2D: Data to Design - Design to Data. Our vision is to provide a forum for researchers and practitioners in computer and systems sciences, data sciences, machine learning, information retrieval, interaction and interface design, and human computer interaction to interact. Our goal is to explore issues surrounding content and presentation personalization across different devices, and to set an agenda for cross-discipline, collaborative engagement.
Categories: Human Computer Interaction
Atish Das Sarma, Nish Parikh, Neel Sundaresan
Tutorial at WWW-2014
Abstract [+]
The focus of this tutorial will be e-commerce product search. Several research challenges appear in this context, both from a research standpoint as well as an application standpoint. We will present various approaches adopted in the industry, review well-known research techniques developed over the last decade, draw parallels to traditional web search highlighting the new challenges in this setting, and dig deep into some of the algorithmic and technical approaches developed for this context. A specific approach that will involve a deep dive into literature, theoretical techniques, and practical impact is that of identifying most suited results quickly from a large database, with settings various across cold start users, and those for whom personalization is possible. In this context, top-k and skylines will be discussed specifically as they form a key approach that spans the web, data mining, and database communities and presents a powerful tool for search across multi-dimensional items with clear preferences within each attribute, like product search as opposed to regular web search.
Categories: Human Computer Interaction
Aditya Khosla, Atish Das Sarma, Raffay Hamid
WWW-2014
Categories: Human Computer Interaction, Vision
Bishan Yang, Nish Parikh, Gyanit Singh, Neel Sundaresan
In ECIR 2014 (To Appear)
Abstract [+]
Query term deletion is one of the commonly used strategies for query rewriting. In this paper, we study the problem of query term deletion using large-scale e-commerce search logs. Especially we focus on queries that do not lead to user clicks and aim to predict a reduced and better query that can lead to clicks by term deletion. Accurate prediction of term deletion can potentially help users recover from poor search results and improve shopping experience. To achieve this,we use various term-dependent and query-dependent measures as features and build a classifier to predict which term is the most likely to be deleted from a given query. Different from previous work on query term deletion, we compute the features not only based on the query history and the available document collection, but also conditioned on the query category, which captures the high-level context of the query. We validate our approach using a large collection of query sessions logs from a leading e-commerce site, and show that it provides promising performance in term deletion prediction, and significantly outperforms baselines that rely on query history and corpus-based statistics without incorporating the query context information.
Si Si, Atish Das Sarma, Elizabeth Churchill, Neel Sundaresan
WWW-2014 (Poster)
Categories: Human Computer Interaction
Atish Das Sarma, Si Si, Elizabeth Churchill, Neel Sundaresan
WWW-2014 (Poster)
Categories: Human Computer Interaction
Mohammad HarisBaig, Vignesh Jagadeesh, Robinson Piramuthu, Anurag Bhardwaj, Wei Di, Neel Sundaresan
WACV 2014: IEEE Winter Conference on Applications of Computer Vision
Abstract [+]
The rapid increase in number of high quality mobile cameras have opened up an array of new problems in mobile vision. Mobile cameras are predominantly monocular and are devoid of any sense of depth, making them heavily reliant on 2D image processing. Understanding 3D structure of scenes being imaged can greatly improve the performance of existing vision/graphics techniques. In this regard, recent availability of large scale RGB-D datasets beg for more effective data driven strategies to leverage the scale of data. We propose a depth recovery mechanism "im2depth", that is lightweight enough to run on mobile platforms, while leveraging the large scale nature of modern RGB-D datasets. Our key observation is to form a basis (dictionary) over the RGB and depth spaces, and represent depth maps by a sparse linear combination of weights over dictionary elements. Subsequently, a prediction function is estimated between weight vectors in RGB to depth space to recover depth maps from query images. A final superpixel post processor aligns depth maps with occlusion boundaries, creating physically plausible results. We conclude with thorough experimentation with four state of the art depth recovery algorithms, and observe an improvement of over 6.5 percent in shape recovery, and over 10cm reduction in average L1 error.
Categories: Vision
Vicente Ordonez, Vignesh Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu
WACV 2014: IEEE Winter Conference on Applications of Computer Vision
Abstract [+]
As the amount of user generated content on the internet grows, it becomes ever more important to come up with vision systems that learn directly from weakly annotated and noisy data. We leverage a large scale collection of user generated content comprising of images, tags and title/captions of furniture inventory from an e-commerce website to discover and categorize learnable visual attributes. Furniture categories have long been the quintessential example of why computer vision is hard, and we make one of the first attempts to understand them through a large scale weakly annotated dataset. We focus on a handful of furniture categories that are associated with a large number of fine-grained attributes. We propose a set of localized feature representations built on top of state-of-the-art computer vision representations originally designed for fine-grained object categorization. We report a thorough empirical characterization on the visual identifiability of various fine-grained attributes using these representations and show encouraging results on finding iconic images and on multi-attribute prediction.
Categories: Vision
CHIMoney (Workshop at CHI-2014)
Categories: Human Computer Interaction
Zitao Liu, Gyanit Singh, Nish Parikh, Neel Sundaresan
In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014.
Abstract [+]
Personalization off ers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics (1) What user information is useful for personalization; (2) Importance of per-query personalization (3) Importance of recency in query prediction. In this paper, we study these problems and provide some preliminary conclusions.