4 research outputs found
Recommending Analogical APIs via Knowledge Graph Embedding
Library migration, which re-implements the same software behavior by using a
different library instead of using the current one, has been widely observed in
software evolution. One essential part of library migration is to find an
analogical API that could provide the same functionality as current ones.
However, given the large number of libraries/APIs, manually finding an
analogical API could be very time-consuming and error-prone. Researchers have
developed multiple automated analogical API recommendation techniques.
Documentation-based methods have particularly attracted significant interest.
Despite their potential, these methods have limitations, such as a lack of
comprehensive semantic understanding in documentation and scalability
challenges. In this work, we propose KGE4AR, a novel documentation-based
approach that leverages knowledge graph (KG) embedding to recommend analogical
APIs during library migration. Specifically, KGE4AR proposes a novel unified
API KG to comprehensively and structurally represent three types of knowledge
in documentation, which can better capture the high-level semantics. Moreover,
KGE4AR then proposes to embed the unified API KG into vectors, enabling more
effective and scalable similarity calculation. We build KGE4AR' s unified API
KG for 35,773 Java libraries and assess it in two API recommendation scenarios:
with and without target libraries. Our results show that KGE4AR substantially
outperforms state-of-the-art documentation-based techniques in both evaluation
scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR
improvements in each scenario). Additionally, we explore KGE4AR' s scalability,
confirming its effective scaling with the growing number of libraries.Comment: Accepted by FSE 202
Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications
throughout the world, the amount of data stored on the Web has been
growing exponentially. In this new digital era, a large number of companies
have emerged with the purpose of ltering the information available on the
web and provide users with interesting items. The algorithms and models
used to recommend these items are called Recommender Systems. These
systems are applied to a large number of domains, from music, books, or
movies to dating or Point-of-Interest (POI), which is an increasingly popular
domain where users receive recommendations of di erent places when
they arrive to a city.
In this thesis, we focus on exploiting the use of contextual information, especially
temporal and sequential data, and apply it in novel ways in both
traditional and Point-of-Interest recommendation. We believe that this type
of information can be used not only for creating new recommendation models
but also for developing new metrics for analyzing the quality of these
recommendations. In one of our rst contributions we propose di erent
metrics, some of them derived from previously existing frameworks, using
this contextual information. Besides, we also propose an intuitive algorithm
that is able to provide recommendations to a target user by exploiting the
last common interactions with other similar users of the system.
At the same time, we conduct a comprehensive review of the algorithms
that have been proposed in the area of POI recommendation between 2011
and 2019, identifying the common characteristics and methodologies used.
Once this classi cation of the algorithms proposed to date is completed, we
design a mechanism to recommend complete routes (not only independent
POIs) to users, making use of reranking techniques. In addition, due to the
great di culty of making recommendations in the POI domain, we propose
the use of data aggregation techniques to use information from di erent
cities to generate POI recommendations in a given target city.
In the experimental work we present our approaches on di erent datasets
belonging to both classical and POI recommendation. The results obtained
in these experiments con rm the usefulness of our recommendation proposals,
in terms of ranking accuracy and other dimensions like novelty, diversity,
and coverage, and the appropriateness of our metrics for analyzing temporal
information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones
en todo el mundo, la cantidad de datos almacenados en la red ha crecido
exponencialmente. En esta nueva era digital, han surgido un gran n umero
de empresas con el objetivo de ltrar la informaci on disponible en la red
y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos
utilizados para recomendar estos art culos reciben el nombre de Sistemas de
Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios,
desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs,
en ingl es), un dominio cada vez m as popular en el que los usuarios reciben
recomendaciones de diferentes lugares cuando llegan a una ciudad.
En esta tesis, nos centramos en explotar el uso de la informaci on contextual,
especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa
tanto en la recomendaci on cl asica como en la recomendaci on de POIs.
Creemos que este tipo de informaci on puede utilizarse no s olo para crear
nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas
m etricas para analizar la calidad de estas recomendaciones. En una de
nuestras primeras contribuciones proponemos diferentes m etricas, algunas
derivadas de formulaciones previamente existentes, utilizando esta informaci
on contextual. Adem as, proponemos un algoritmo intuitivo que es
capaz de proporcionar recomendaciones a un usuario objetivo explotando
las ultimas interacciones comunes con otros usuarios similares del sistema.
Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que
se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y
2019, identi cando las caracter sticas comunes y las metodolog as utilizadas.
Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la
fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo
POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking.
Adem as, debido a la gran di cultad de realizar recomendaciones en el
ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos
para utilizar la informaci on de diferentes ciudades y generar recomendaciones
de POIs en una determinada ciudad objetivo.
En el trabajo experimental presentamos nuestros m etodos en diferentes
conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los
resultados obtenidos en estos experimentos con rman la utilidad de nuestras
propuestas de recomendaci on en t erminos de precisi on de ranking y de
otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de
apropiadas son nuestras m etricas para analizar la informaci on temporal y
los sesgos en las recomendaciones producida