Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling search engine effectiveness for federated search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Using query logs to establish vocabularies in distributed information retrieval
Information Processing and Management: an International Journal
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Good abandonment in mobile and PC internet search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Adaptation of offline vertical selection predictions in the presence of user feedback
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
On the local optimality of LambdaRank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
User behavior driven ranking without editorial judgments
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 20th international conference on World wide web
Aggregated search result diversification
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Learning to aggregate vertical results into web search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Evaluating large-scale distributed vertical search
Proceedings of the 9th workshop on Large-scale and distributed informational retrieval
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Evaluating aggregated search pages
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Task complexity, vertical display and user interaction in aggregated search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
The effect of aggregated search coherence on search behavior
Proceedings of the 21st ACM international conference on Information and knowledge management
Evaluating reward and risk for vertical selection
Proceedings of the 21st ACM international conference on Information and knowledge management
Using intent information to model user behavior in diversified search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
A unified search federation system based on online user feedback
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Which vertical search engines are relevant?
Proceedings of the 22nd international conference on World Wide Web
On the reliability and intuitiveness of aggregated search metrics
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Factors affecting aggregated search coherence and search behavior
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Aggregated search: A new information retrieval paradigm
ACM Computing Surveys (CSUR)
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Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals could be relevant, which makes the placement of these vertical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities? In this paper we present a machine-learning framework for SERP composition in the presence of multiple relevant verticals. First, instead of using the traditional label generation method of human judgment guidelines and trained judges, we use a randomized online auditioning system that allows us to evaluate triples of the form query, web block, vertical. We use a pairwise click preference to evaluate whether the web block or the vertical block had a better users' engagement. Next, we use a hinged feature vector that contains features from the web block to create a common reference frame and augment it with features representing the specific vertical judged by the user. A gradient boosted decision tree is then learned from the training data. For the final composition of the SERP, we place a vertical result at a slot if the score is higher than a computed threshold. The thresholds are algorithmically determined to guarantee specific coverage for verticals at each slot. We use correlation of clicks as our offline metric and show that click-preference target has a better correlation than human judgments based models. Furthermore, on online tests for News and Image verticals we show higher user engagement for both head and tail queries.