904 research outputs found
Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress
In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature. In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected. In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings. In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration. We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration. The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results. Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.Ministerio de Economía y Competitividad RTI2018-101204-B-C22Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-RMinisterio de Economía, Industria y Competitividad MCIU-AEI TIN2017-90644-RED
SIGIR 2021 E-Commerce Workshop Data Challenge
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for
"In-session prediction for purchase intent and recommendations". The challenge
addresses the growing need for reliable predictions within the boundaries of a
shopping session, as customer intentions can be different depending on the
occasion. The need for efficient procedures for personalization is even clearer
if we consider the e-commerce landscape more broadly: outside of giant digital
retailers, the constraints of the problem are stricter, due to smaller user
bases and the realization that most users are not frequently returning
customers. We release a new session-based dataset including more than 30M
fine-grained browsing events (product detail, add, purchase), enriched by
linguistic behavior (queries made by shoppers, with items clicked and items not
clicked after the query) and catalog meta-data (images, text, pricing
information). On this dataset, we ask participants to showcase innovative
solutions for two open problems: a recommendation task (where a model is shown
some events at the start of a session, and it is asked to predict future
product interactions); an intent prediction task, where a model is shown a
session containing an add-to-cart event, and it is asked to predict whether the
item will be bought before the end of the session.Comment: SIGIR eCOM 2021 Data Challeng
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