Users' perception of the performance of a filtering system
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Users' criteria for relevance evaluation: a cross-situational comparison
Information Processing and Management: an International Journal
Communications of the ACM
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The concept of relevance in IR
Journal of the American Society for Information Science and Technology
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A large-scale analysis of query logs for assessing personalization opportunities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
The influence of caption features on clickthrough patterns in web search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
How well does result relevance predict session satisfaction?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
PSkip: estimating relevance ranking quality from web search clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
An elaborated model of social search
Information Processing and Management: an International Journal
Enhanced results for web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Social annotations in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Social features are increasingly integrated within the search results page of the main commercial search engines. There is, however, little understanding of the utility of social features in traditional search. In this paper, we study utility in the context of social annotations, which are markings indicating that a person in the social network of the user has liked or shared a result document. We introduce a taxonomy of social relevance aspects that influence the utility of social annotations in search, spanning query classes, the social network, and content relevance. We present the results of a user study quantifying the utility of social annotations and the interplay between social relevance aspects. Through the user study we gain insights on conditions under which social annotations are most useful to a user. Finally, we present machine learned models for predicting the utility of a social annotation using the user study judgments as an optimization criterion. We model the learning task with features drawn from web usage logs, and show empirical evidence over real-world head and tail queries that the problem is learnable and that in many cases we can predict the utility of a social annotation.