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

Neighbor Selection and Weighting in User-Based Collaborative Filtering: A Performance Prediction Approach

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on the Web, http://dx.doi.org/10.1145/2579993User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this article we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the preceding generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-of-the-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor-scoring-powered versions of a user-based collaborative filtering algorithm.This research was supported by the Spanish Ministry of Science and Research (TIN2011-28538-C02-01). Part of this work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme, funded by European Comission FP7 grant agreement no. 246016

Multi-style explainable matrix factorization techniques for recommender systems.

Black-box recommender system models are machine learning models that generate personalized recommendations without explaining how the recommendations were generated to the user or giving them a way to correct wrong assumptions made about them by the model. However, compared to white-box models, which are transparent and scrutable, black-box models are generally more accurate. Recent research has shown that accuracy alone is not sufficient for user satisfaction. One such black-box model is Matrix Factorization, a State of the Art recommendation technique that is widely used due to its ability to deal with sparse data sets and to produce accurate recommendations. Recent work has proposed new Matrix Factorization models that are explainable by incorporating explanations derived from semantic knowledge graphs, user neighborhood, or item neighborhood graphs into the model learning process. These Explainable Matrix Factorization (EMF) methods have the benefit of providing explanations without sacrificing accuracy. However, their explanations tend to be limited to only one explanation style. In this dissertation, we propose a framework comprising new machine learning methods to build explainable models that can make recommendations with multiple explanation-styles, by hybridizing multiple EMF models and by proposing new EMF models that explain recommendations using tags. The various pre-calculated explainability scores, leveraged in our proposed methods, have all been validated in prior work that conducted user studies to evaluate users’ satisfaction with each style individually. Unlike most existing work that generates explanations post-hoc, i.e., after the predictions have already been made, our framework is based on calculating explainability scores directly from available data, before the model is learned, and then using them as part of a regularization mechanism, to guide the model learning. Unlike post-hoc methods, our framework makes it possible to learn machine learning models that take into account the explanation scores, therefore ensuring higher transparency. Our evaluation experiments show that our proposed methods provide accurate recommendations while also providing users with multiple styles of explanations about how data was used to generate each recommendation. Each explanation style also provides additional decision-making information that empowers the user to either trust or scrutinize the recommendations. Although, rooted in the hybrid recommendation framework, our proposed methods make a significant step forward in explainable AI and beyond existing hybrid frameworks, because the proposed hybridization mechanisms make an intentional effort to take into account the individual models’ explanations and not only their output predicted ratings

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity