Important Papers Sample Clauses

Important Papers. Since SWIRL 2012 As part of the homework assignment, we asked participants to select one paper from within their area of expertise and one paper from outside of their area of expertise that they considered important for the information retrieval community. We manually classified all papers in order to understand the participants’ perspective on recent research. The complete set of papers in these categories can be found at the end of this manuscript. Table 3 shows the number of papers suggested by category in the homework responses. As expected based on the RSVP data, many participants selected papers from the machine learning community when asked about papers outside of the Core IR community. Deep learning was recognized as a fundamental tool that had powered significant advances in other fields. Sixteen participants cited Xxxxxxx’s word2vec paper as an important recent contribution [MSC+13]. Another important theme was stateful search, defined to include conversational search and other multi-turn information access. Ten participants selected Xxxxxxxxx and Xxxxxxxx’x theoretical model for conversational search [RC17]. The social implications of information access systems are beginning to get increased attention more broadly in the academic community [BS16]. Common themes in this area include algorithmic bias, ethics, and transparency. While there were no papers recommended by multiple participants, the subfield of Responsible Information Retrieval is growing. There were two themes related to evaluation: experimentation and off-policy evaluation. While several of the participants selected Xxxxxxx Xxxxx’x meta-analysis of previously-published results [Sak16a], many participants recognized issues with replicating and reproducing results, perhaps inspired by recent reproducibility concerns in psychology [Ope15]. The second theme concerned reuse of production log data for evaluating new treatments (e.g. algorithms, parameters). This problem occurs often when evaluating and training retrieval models in industry and has been receiving attention in the machine learning community. Several participants cited the work of Xxxxxxxx Xxxxxxxx and his students as representative of this area [JSS17]. The issue of quantifying the effect of unjudged documents was also a common theme, with Rank-Biased Precision [MZ08] and its successor INST [MBST17] being examples of weighted-precision metrics in which this ability was specifically explored. Finally, an efficiency theme also...
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