79 research outputs found
A Constraint Programming Approach to Automatic Layout Definition for Search Results
In this paper we describe a general framework based on constraint programming techniques to address the automatic layout definition problem for Web search result pages, considering heterogeneous result items types (e.g., web links, images, videos, maps, etc.). Starting from the entity type(s) specified in the search query and the result types deemed more relevant for the given entity type, we define an optimization problem and a set of constraints that grant the optimal positioning of results in the page, modeled as a grid with assigned weights depending on the visibility
Interacting Attention-gated Recurrent Networks for Recommendation
Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen accidentally. More importantly, they learn user and item dynamics
separately, thus failing to capture their joint effects on user-item
interactions. To better model user and item dynamics, we present the
Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
model to measure the relevance of each time step. In particular, we propose a
novel attention scheme to learn the attention scores of user and item history
in an interacting way, thus to account for the dependencies between user and
item dynamics in shaping user-item interactions. By doing so, IARN can
selectively memorize different time steps of a user's history when predicting
her preferences over different items. Our model can therefore provide
meaningful interpretations for recommendation results, which could be further
enhanced by auxiliary features. Extensive validation on real-world datasets
shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge
Management (CIKM), 201
Metadata Representations for Queryable ML Model Zoos
Machine learning (ML) practitioners and organizations are building model zoos
of pre-trained models, containing metadata describing properties of the ML
models and datasets that are useful for reporting, auditing, reproducibility,
and interpretability purposes. The metatada is currently not standardised; its
expressivity is limited; and there is no interoperable way to store and query
it. Consequently, model search, reuse, comparison, and composition are
hindered. In this paper, we advocate for standardized ML model meta-data
representation and management, proposing a toolkit supported to help
practitioners manage and query that metadata
Towards a Top-K SPARQL Query Benchmark Generator
The research on optimization of top-k SPARQL query would largely benefit from the establishment of a benchmark that allows comparing different approaches. For such a benchmark to be meaningful, at least two requirements should hold: 1) the benchmark should resemble reality as much as possible, and 2) it should stress the features of the topk SPARQL queries both from a syntactic and performance perspective. In this paper we propose Top-k DBPSB: an extension of the DBpedia SPARQL benchmark (DBPSB), a benchmark known to resemble reality, with the capabilities required to compare SPARQL engines on top-k queries.Web Information System
How do crowdworker communities and microtask markets influenceeach other? a data-driven study on amazon mechanical turk
Crowdworker online communities -- operating in fora like mTurkForum and TurkerNation -- are an important actor in microwork markets. Albeit central to market dynamics, how the behavior of crowdworker communities and the dynamics of online marketplaces influence each other is yet to be understood. To provide quantitative evidence of such influence, we performed an analysis on 6-years worth of mTurk market activities and community discussions in six fora. We investigated the nature of the relationships that exist between activities in fora, tasks published in mTurk, requesters for such tasks, and task completion speed. We validate -- and expand upon -- results from previous work by showing that (i) there are differences between market demand and community activities that are specific to fora and task types; (ii) the temporal progression of HIT availability in the market is predictive of the upcoming amount of crowdworker discussions, with significant differences across fora and discussion categories; (iii) activities in fora can have a significant positive impact on the completion speed of tasks available in the market
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