NDLI: Learning to rank with multi-aspect relevance for vertical search

Please wait, while we are loading the content...

Nowcasting the macroeconomy with search engine data

Spatially-aware indexing for image object retrieval

Beyond 100 million entities: large-scale blocking-based resolution for heterogeneous data

No search result left behind: branching behavior with browser tabs

WebSets: extracting sets of entities from the web using unsupervised information extraction

Sponsored search auctions with conflict constraints

Probabilistic models for personalizing web search

Finding the right consumer: optimizing for conversion in display advertising campaigns

Correlating financial time series with micro-blogging activity

On clustering heterogeneous social media objects with outlier links

Effects of user similarity in social media

The secret life of social links

Exploration and discovery of user-generated content in large information spaces

Mining, searching and exploiting collaboratively generated content on the web

2nd international workshop on diversity in document retrieval (DDR 2012)

Auralist: introducing serendipity into music recommendation

Mining contrastive opinions on political texts using cross-perspective topic model

Characterizing web content, user interests, and search behavior by reading level and topic

Selecting actions for resource-bounded information extraction using reinforcement learning

Post-click conversion modeling and analysis for non-guaranteed delivery display advertising

Effective query formulation with multiple information sources

Fast top-k retrieval for model based recommendation

Harmony and dissonance: organizing the people's voices on political controversies

Adding semantics to microblog posts

How user behavior is related to social affinity

Computational advertising: leveraging user interaction & contextual factors for improved ad relevance & targeting

Collaborative information seeking: understanding users, systems, and content

WSCD 2012: workshop on web search click data 2012

Efficient misbehaving user detection in online video chat services

Coupled temporal scoping of relational facts

Topical clustering of search results

Online selection of diverse results

Incorporating revisiting behaviors into click models

Learning to rank with multi-aspect relevance for vertical search

Relational click prediction for sponsored search

Identifying content for planned events across social media sites

Exploring social influence via posterior effect of word-of-mouth recommendations

Finding your friends and following them to where you are

The early bird gets the buzz: detecting anomalies and emerging trends in information networks

Machine learning for query-document matching in search

Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial and temporal similarities

Overcoming browser cookie churn with clustering

To each his own: personalized content selection based on text comprehensibility

Overlapping clusters for distributed computation

A noise-aware click model for web search

Beyond ten blue links: enabling user click modeling in federated web search

"I loan because...": understanding motivations for pro-social lending

Daily deals: prediction, social diffusion, and reputational ramifications

Find me opinion sources in blogosphere: a unified framework for opinionated blog feed retrieval

How to win friends and influence people, truthfully: influence maximization mechanisms for social networks

Characterizing and harnessing peer-production of information in social tagging systems

Object matching in tweets with spatial models

mTrust: discerning multi-faceted trust in a connected world

Personalized click model through collaborative filtering

Understanding cyclic trends in social choices

Inferring social ties across heterogenous networks

Of hammers and nails: an empirical comparison of three paradigms for processing large graphs

Fair and balanced: learning to present news stories

Maximizing product adoption in social networks

Pairwise cross-domain factor model for heterogeneous transfer ranking

Extracting search-focused key n-grams for relevance ranking in web search

Answers, not links: extracting tips from yahoo! answers to address how-to web queries

Scalable inference in latent variable models

Query suggestion by constructing term-transition graphs

A straw shows which way the wind blows: ranking potentially popular items from early votes

Learning recommender systems with adaptive regularization

Language models for keyword search over data graphs

A large-scale sentiment analysis for Yahoo! answers

Collaborative ranking

Large-scale analysis of individual and task differences in search result page examination strategies

Tips, dones and todos: uncovering user profiles in foursquare

From chatter to headlines: harnessing the real-time web for personalized news recommendation

Sequence clustering and labeling for unsupervised query intent discovery

When will it happen?: relationship prediction in heterogeneous information networks

ETF: extended tensor factorization model for personalizing prediction of review helpfulness

IR system evaluation using nugget-based test collections

The life and death of online groups: predicting group growth and longevity

Multi-relational matrix factorization using bayesian personalized ranking for social network data

Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries

Evaluating search in personal social media collections

Comment spam detection by sequence mining

Domain bias in web search

Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization

Mining slang and urban opinion words and phrases from cQA services: an optimization approach

Optimized top-k processing with global page scores on block-max indexes

What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities

Learning to rank with multi-aspect relevance for vertical search

Content Provider	ACM Digital Library
Author	Tseng, Belle Wang, Xuanhui Kang, Changsung Chang, Yi
Abstract	Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects (e.g., text matching and distance in local search). Thus the overall relevance between a query and a document is a tradeoff between multiple relevance aspects. Such a tradeoff can vary for different types of queries or in different contexts. In this paper, we explore these vertical-specific aspects in the learning to rank setting. We propose a novel formulation in which the relevance between a query and a document is assessed with respect to each aspect, forming the multi-aspect relevance. In order to compute a ranking function, we study two types of learning-based approaches to estimate the tradeoff between these relevance aspects: a label aggregation method and a model aggregation method. Since there are only a few aspects, a minimal amount of training data is needed to learn the tradeoff. We conduct both offline and online test experiments on a local search engine and the experimental results show that our proposed multi-aspect relevance formulation is very promising. The two types of aggregation methods perform more effectively than a set of baseline methods including a conventional learning to rank method.
Starting Page	453
Ending Page	462
Page Count	10
File Format	PDF MP4
ISBN	9781450307475
DOI	10.1145/2124295.2124350
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-02-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Aggregation Multi-aspect relevance Web search
Content Type	Audio Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning to Rank with Multi-Aspect Relevance for Vertical Search

Multi-task learning for learning to rank in web search

Learning to rank with multiple objective functions

Quality-biased ranking for queries with commercial intent

From Web Search Relevance to Vertical Search Relevance

Transferring Learning To Rank Models for Web Search

Learning to aggregate vertical results into web search results

Cross-corpus relevance projection

Are click-through data adequate for learning web search rankings?

Learning to rank with multi-aspect relevance for vertical search

Similar Documents

Learning to Rank with Multi-Aspect Relevance for Vertical Search

Multi-task learning for learning to rank in web search

Learning to rank with multiple objective functions

Quality-biased ranking for queries with commercial intent

From Web Search Relevance to Vertical Search Relevance

Transferring Learning To Rank Models for Web Search

Learning to aggregate vertical results into web search results

Cross-corpus relevance projection

Are click-through data adequate for learning web search rankings?

Learning to rank with multi-aspect relevance for vertical search