Synthesis Lectures on Information Concepts, Retrieval, and Services

By Daniel Tunkelang, Mike Thelwall. 2009

381 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4c6cea47e1d6d618b30d0722837b5b860b5cbb68

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Citations in the corpus

Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, background): “As to future work, we plan to explore whether ranking-based strategies that integrate our sCVR model can enhance the performance of item recommendation and whether a combination with interaction data [9] can help to improve performance even further.”
Neural information retrieval: at the end of the early years (2017, background): “(2016b) propose a distributed representation which captures the user information need as sequence of vector states instead of traditional binary event used in a probabilistic graphical model (PGM) based click model (Chuklin et al. 2015).”
Understanding the Long-Term Use of Smart Speaker Assistants (2018, methodology): “We applied a standard 10-minute idle time session boundary [13-15, 23] to segment the data to examine how many commands were used in each interaction with the device, as described in [18].”
Ontop: Answering SPARQL queries over relational databases (2016, background): “, OptiqueVQS [58], QueryVOWL [24], SEWASIE VQS [15], and Faceted search [60].”
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video (2019, background): “The multi-modal representation is one of the most important problem in multi-modal applications [27].”
Unbiased Learning-to-Rank with Biased Feedback (2016, methodology): “Other click models ([5, 3, 1], also see [4]) have been proposed, and are trained to maximize log-likelihood of observed clicks.”