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Found 11 papers, 2 papers with code
The Potential of Learned Index Structures for Index Compression
Second, we evaluate the potential gains that can be achieved in terms of memory requirements.
Optimizing Ranking Models in an Online Setting
Our findings show that the theoretical bounds of DBGD do not apply to any common ranking model and, furthermore, that the performance of DBGD is substantially worse than PDGD in both ideal and worst-case circumstances.
A Collective Variational Autoencoder for Top-$N$ Recommendation with Side Information
Learning feature representations, on the other hand, ensures a sufficient number of inputs to train a deep network.
Why People Search for Images using Web Search Engines
(2)How does image search behavior change with user intent?
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
Conversely, simpler models can be optimized on fewer interactions and thus provide a better user experience, but they will converge towards suboptimal rankings.
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
We show empirically that, compared to previous multileaved comparison methods, PPM is more sensitive to user preferences and scalable with the number of rankers being compared.
Towards Learning Reward Functions from User Interactions
It is natural to represent the behavior of users who are engaging with interactive systems such as a search engine or a recommender system, as a sequence of actions where each next action depends on the current situation and the user reward of taking a particular action.
Evaluating Personal Assistants on Mobile devices
Mobile devices differ radically from classic command-based and point-and-click user interfaces, now allowing for gesture-based interaction using fine-grained touch and swipe signals.
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
We propose a hierarchical re-estimation approach for topic models to combat generality and impurity; the proposed approach operates at three levels: words, topics, and documents.
Document Filtering for Long-tail Entities
Results of applying the model to unseen entities are promising, indicating that the model is able to learn the general characteristics of a vital document.
The Anatomy of Relevance: Topical, Snippet and Perceived Relevance in Search Result Evaluation
Currently, the quality of a search engine is often determined using so-called topical relevance, i. e., the match between the user intent (expressed as a query) and the content of the document.
