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Royi Ronen, Gal Lavee, and Elad Yom-Tov

Collaborative filtering (CF) recommendation systems are one of the most popular and successful methods for recommending products to people. CF systems work by finding similarities between different people according to their past purchases, and using these similarities to suggest possible items of interest. Here we investigate how CF systems can be enhanced using Internet browsing data and search engine query logs, both of which represent a rich profile of individuals’ interests. We introduce two...

Publication details
Date: 16 May 2016
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Jaime Teevan, Shamsi T. Iqbal, and Curtis von Veh

This paper presents the MicroWriter, a system that decomposes the task of writing into three types of microtasks to produce a single report: 1) generating ideas, 2) labeling ideas to organize them, and 3) writing paragraphs given a few related ideas. Because each microtask can be completed individually with limited awareness of what has been already done and what others are doing, this decomposition can change the experience of collaborative writing. Prior work has used microtasking to support...

Publication details
Date: 1 May 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana

This paper investigates the popular neural word embedding method Word2vec as a source of evidence in document ranking. In contrast to NLP applications of word2vec, which tend to use only the input embeddings, we retain both the input and the output embeddings, allowing us to calculate a different word similarity that may be more suitable for document ranking. We map the query words into the input space and the document words into the output space, and compute a relevance score by aggregating the cosine...

Publication details
Date: 11 April 2016
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
Sandro Bauer, Filip Radlinski, and Ryen W. White

People commonly need to purchase things in person, from large garden supplies to home decor. Although modern search systems are very effective at finding online products, little research attention has been paid to helping users find places that sell a specific product offline. For instance, users searching for an apron are not typically directed to a nearby kitchen store by a standard search engine.

In this paper, we investigate "where can I buy"-style queries related to...

Publication details
Date: 1 April 2016
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
Publication details
Date: 1 April 2016
Type: Inproceeding
Publication details
Date: 1 April 2016
Type: Inproceeding
Publisher: WWW – World Wide Web Consortium (W3C)
Rishiraj Saha Roy, Anusha Suresh, Niloy Ganguly, and Monojit Choudhury

In this research, we explore nested or hierarchical query segmentation4, where segments are defined recursively as consisting of contiguous sequences of segments or query words, as a more effective representation of a query. We design a lightweight and unsupervised nested segmentation scheme, and propose how to use the tree arising out of the nested representation of a query to improve ranking performance. We show that nested segmentation can lead to significant gains over stateof-the-art at...

Publication details
Date: 1 March 2016
Type: Inproceeding
Publisher: ECIR
Liu Yang, Qi Guo, Yang Song, Sha Meng, Milad Shokouhi, Kieran McDonald, and W. Bruce Croft

Proactive search systems like Google Now and Microsoft Cortana have gained increasing popularity with the growth of mobile Internet. Unlike traditional reactive search systems where search engines return results in response to queries issued by the users, proactive systems actively push information cards to the users on mobile devices based on the context around time, location, environment (e.g., weather), and user interests. A proactive system is a zero-query information retrieval system, which makes...

Publication details
Date: 1 March 2016
Type: Inproceeding
Zhaohui Wu, Yang Song, and C. Lee Giles

Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity...

Publication details
Date: 1 February 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Bo Wu, Tao Mei, Wen-Huang Cheng, and Yongdong Zhang

Time information plays a crucial role on social media popularity. Existing research on popularity prediction, effective though, ignores temporal information which is highly related to user-item associations and thus often results in limited success. An essential way is to consider all these factors (user, item, and time), which capture the dynamic nature of photo popularity. In this paper, we present a novel approach to factorize the popularity into user-item context and time-sensitive context for...

Publication details
Date: 1 February 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Yubin Kim, Kevyn Collins-Thompson, and Jaime Teevan

Despite technological advances, algorithmic search systems still have difficulty with complex or subtle information needs. For example, scenarios requiring deep semantic interpretation are a challenge for computers. People, on the other hand, are well-suited to solving such problems. As a result, there is an opportunity for humans and computers to collaborate during the course of a search in a way that takes advantage of the unique abilities of each. While search tools that rely on human intervention...

Publication details
Date: 1 January 2016
Type: Article
Publisher: ACM – Association for Computing Machinery
Rishiraj Saha Roy, Anusha Suresh, Niloy Ganguly, Monojit Choudhury, Deepak Shankar, and Tanwita Nimiar

Past work on query segmentation has exclusively focused on at or non-hierarchical segmentation, where query words are simply partitioned into non-overlapping contiguous chunks of words. Such an approach suffers from the problem of granularity, and consequent difficulties in IR application. Here, we explore nested or hierarchical query segmentation, where segments are defined recursively as consisting of contiguous sequences of segments or query words, as an effective and powerful alternative...

Publication details
Date: 31 December 2015
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2015-91
Alistair Moffat, Peter Bailey, Falk Scholer, and Paul Thomas

A large number of metrics have been proposed to measure the effectiveness of information retrieval systems. Here we provide a detailed explanation of one recent proposal, INST, articulate the various properties that it embodies, and describe a number of pragmatic issues that need to be taken in to account when writing an implementation. The result is a specification for a program inst_eval for use in TREC-style IR experimentation.

Publication details
Date: 8 December 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Paul Thomas, Peter Bailey, Alistair Moffat, and Falk Scholer

Information retrieval systems are often evaluated through the use of effectiveness metrics. In the past, the metrics used have corresponded to fixed models of user behavior, presuming, for example, that the user will view a predetermined number of items in the search engine results page, or that they have a constant probability of advancing from one item in the result page to the next. Recently, a number of proposals for models of user behavior have emerged that are parameterized in terms of the number...

Publication details
Date: 2 December 2015
Type: Inproceeding
Publisher: Springer
Ting Yao, Tao Mei, and Chong-Wah Ngo

One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the query and image. The research on this topic has evolved through two paradigms: feature-based vector model and image ranker learning. The former relies on the image surrounding texts, while the latter learns a ranker based on human labeled query-image pairs. Each of the paradigms has its own limitation. The vector model is sensitive to the quality of text descriptions, and the learning paradigm...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: IEEE International Conference on Computer Vision
Royal Sequiera, Monojit Choudhury, Parth Gupta, Paolo Rosso, Shubham Kumar, Somnath Banerjee, Sudip Kumar Naskar, Sivaji Bandyopadhyay, Gokul Chittaranjan, Amitava Das, and Kunal Chakma

The Transliterated Search track has been organized for the third year in FIRE-2015. The track had three subtasks. Subtask I was on language labeling of words in code-mixed text fragments; it was conducted for 8 Indian languages: Bangla, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil, Telugu, mixed with English. Subtask II was on ad-hoc retrieval of Hindi film lyrics, movie reviews and astrology documents, where both the queries and documents were either in Hindi written in Devanagari or in Roman...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: FIRE
Fei LV, hongyu zhang, Jian-Guang LOU, Shaowei WANG, Dongmei ZHANG, and Jianjun ZHAO
Publication details
Date: 1 November 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM
Alistair Moffat, Falk Scholer, Paul Thomas, and Peter Bailey

Evaluation of information retrieval systems with test collections makes use of a suite of fixed resources: a document corpus; a set of topics; and associated judgments of the relevance of each document to each topic. With large modern collections, exhaustive judging is not feasible. Therefore an approach called pooling is typically used where, for example, the documents to be judged can be determined by taking the union of all documents returned in the top positions of the answer lists returned...

Publication details
Date: 1 October 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Bhaskar Mitra and Nick Craswell

Query auto-completion (QAC) systems typically suggest queries that have previously been observed in search logs. Given a partial user query, the system looks up this query prefix against a precomputed set of candidates, then orders them using ranking signals such as popularity. Such systems can only recommend queries for prefixes that have been previously seen by the search engine with adequate frequency. They fail to recommend if the prefix is sufficiently rare such that it has no matches in the...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Jianpeng Cheng, Zhongyuan Wang, Ji-Rong Wen, Jun Yan, and Zheng Chen

Representing discrete words in a continuous vector space turns out to be useful for natural language applications related to text understanding. Meanwhile, it poses extensive challenges, one of which is due to the polysemous nature of human language. A common solution (a.k.a word sense induction) is to separate each word into multiple senses and create a representation for each sense respectively. However, this approach is usually computationally expensive and prone to data sparsity, since each sense...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Zhongyuan Wang, Haixun Wang, Ji-Rong Wen, and Yanghua Xiao

Humans understand the world by classifying objects into an appropriate level of categories. This process is often automatic and subconscious. Psychologists and linguists call it as Basic-level Categorization (BLC). BLC can benefit lots of applications such as knowledge panel, advertising and recommendation. However, how to quantify basic-level concepts is still an open problem. Recently, much work focuses on constructing knowledge bases or semantic networks from web scale text corpora, which makes it...

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Bhanu Vattikonda, Vacha Dave, Saikat Guha, and Alex C. Scoeren
Publication details
Date: 1 October 2015
Type: Inproceeding
Vasileios Lampos, Elad Yom-Tov, Richard Pebody, and Ingemar J. Cox

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of usergenerated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the...

Publication details
Date: 7 September 2015
Type: Article
Publisher: Springer
Publication details
Date: 1 September 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
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