578 research outputs found
Use of implicit graph for recommending relevant videos: a simulated evaluation
In this paper, we propose a model for exploiting community based usage information for video retrieval. Implicit usage information from a pool of past users could be a valuable source to address the difficulties caused due to the semantic gap problem. We propose a graph-based implicit feedback model in which all the usage information can be represented. A number of recommendation algorithms were suggested and experimented. A simulated user evaluation is conducted on the TREC VID collection and the results are presented. Analyzing the results we found some common characteristics on the best performing algorithms, which could indicate the best way of exploiting this type of usage information
Training Curricula for Open Domain Answer Re-Ranking
In precision-oriented tasks like answer ranking, it is more important to rank
many relevant answers highly than to retrieve all relevant answers. It follows
that a good ranking strategy would be to learn how to identify the easiest
correct answers first (i.e., assign a high ranking score to answers that have
characteristics that usually indicate relevance, and a low ranking score to
those with characteristics that do not), before incorporating more complex
logic to handle difficult cases (e.g., semantic matching or reasoning). In this
work, we apply this idea to the training of neural answer rankers using
curriculum learning. We propose several heuristics to estimate the difficulty
of a given training sample. We show that the proposed heuristics can be used to
build a training curriculum that down-weights difficult samples early in the
training process. As the training process progresses, our approach gradually
shifts to weighting all samples equally, regardless of difficulty. We present a
comprehensive evaluation of our proposed idea on three answer ranking datasets.
Results show that our approach leads to superior performance of two leading
neural ranking architectures, namely BERT and ConvKNRM, using both pointwise
and pairwise losses. When applied to a BERT-based ranker, our method yields up
to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model
trained without a curriculum). This results in models that can achieve
comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
Commentary:Prevention and management of delirium in older Australians: The need for the integration of carers as partners in care
Report on the future conversations workshop at CHIIR 2021
The Future Conversations workshop at CHIIR’21 looked to the future of search, recommen- dation, and information interaction to ask: where are the opportunities for conversational interactions? What do we need to do to get there? Furthermore, who stands to benefit?The workshop was hands-on and interactive. Rather than a series of technical talks, we solicited position statements on opportunities, problems, and solutions in conversational search in all modalities (written, spoken, or multimodal). This paper –co-authored by the organisers and participants of the workshop– summarises the submitted statements and the discussions we had during the two sessions of the workshop. Statements discussed during the workshop are available at https://bit.ly/FutureConversations2021Statements
Optimal Timing and Legal Decisionmaking: The Case of the Liquidation Decision in Bankruptcy
- …