Situational Context for Ranking in Personal Search

By Hamed Zamani, Michael Bendersky, Xuanhui Wang, Mingyang Zhang. 2017

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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In Management Science and Operations Research

Citations in the corpus

Relevance-based Word Embedding (2017, methodology): “For instance, Zamani et al. [39] trained a context-aware ranking model in which the embedding vectors of frequent n-grams are learned using click data.”
Neural Ranking Models with Weak Supervision (2017, background): “ry, which this paper belongs to, consists of the approaches that design and train a (deep) neural network for a speci•c task, e.g., question answering [6, 41], click models [4], context-aware ranking [42], etc. A number of the approaches in this category have been proposed for ranking documents in r”
Position Bias Estimation for Unbiased Learning to Rank in Personal Search (2018, background): “Our work is related to recent work on personal search [2, 3, 6, 22, 33] that emphasized relevance-based over time-based ranking [6].”
Current challenges and visions in music recommender systems research (2017, background): “It is worth noting that situational features have been proven to be strong signals in improving retrieval performance in search engines [16,190].”
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (2018, methodology): “In this section, we demonstrate the effectiveness of our library for two real-world ranking scenarios: Gmail search [40, 47] and document recommendation in Google Drive [38].”
A Deep Look into Neural Ranking Models for Information Retrieval (2019, background): “• Situational context: the properties of the current search request, independent from the query content, such as location and time [147, 148].”
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (2018, unknown): “Neural RankingModels Several recent studies have applied deep neural network methods to a number of IR tasks, including question answering [51], ad-hoc retrieval [34, 49], and context-aware ranking [55].”