Recommendation Systems Based on Association Rule Mining for a Target Object by Evolutionary Algorithms

Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then the Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality

Corrupted Contextual Bandits with Action Order Constraints

We consider a variant of the novel contextual bandit problem with corrupted context, which we call the contextual bandit problem with corrupted context and action correlation, where actions exhibit a relationship structure that can be exploited to guide the exploration of viable next decisions. Our setting is primarily motivated by adaptive mobile health interventions and related applications, where users might transitions through different stages requiring more targeted action selection approaches. In such settings, keeping user engagement is paramount for the success of interventions and therefore it is vital to provide relevant recommendations in a timely manner. The context provided by users might not always be informative at every decision point and standard contextual approaches to action selection will incur high regret. We propose a meta-algorithm using a referee that dynamically combines the policies of a contextual bandit and multi-armed bandit, similar to previous work, as wells as a simple correlation mechanism that captures action to action transition probabilities allowing for more efficient exploration of time-correlated actions. We evaluate empirically the performance of said algorithm on a simulation where the sequence of best actions is determined by a hidden state that evolves in a Markovian manner. We show that the proposed meta-algorithm improves upon regret in situations where the performance of both policies varies such that one is strictly superior to the other for a given time period. To demonstrate that our setting has relevant practical applicability, we evaluate our method on several real world data sets, clearly showing better empirical performance compared to a set of simple algorithms

Estrategias de calentamiento en bandidos multi-brazo para recomendación

Trabajo Fin de Máster en Investigación e Innovación en Inteligencia Computacional y Sistemas InteractivosRecommender systems have become an essential piece of multiple online platforms such as streaming services and e-commerce in the last years as they provide users with articles they may find interesting and thus granting them a personalised experience. The recommendation problem has many opened investigation lines. One of them is the topic we tackle in this work: the cold-start problem. In the context of recommender systems the cold-start problem refers to the situation in which a system does not have enough information to give proper suggestions to the user. The cold-start problem often occurs because of the following three main reasons: the user to be recommended is new to the system and thus there is no information about its likes, some of the items that are recommended have been recently added to the system and they do not have users’ reviews, or the system is completely new and there is no information about the users nor the items. Classical recommendation techniques come from Machine learning and they understand recommendation as an static process in which the system provides suggestions to the user and the last rates them. It is more convenient to understand recommendation as a cycle of constant interaction between the user and the system and every time a user rates an item, the system uses it to learn from the user. In that sense we can sacrifice immediate reward in order to earn information about the user and improve long term reward. This schema establishes a balance between exploration (non-optimal recommendations to learn about the user) and exploitation (optimal recommendations to maximise the reward). Techniques known as multi-armed bandits are used to get that balance between exploration and exploitation and we propose them to tackle cold-start problem. Our hypothesis is that an exploration in the first epochs of the recommendation cycle can lead to an improvement in the reward during the latest epochs. To test this hypothesis we divide the recommendation loop in two phases: the warm-up, in which we follow a more exploratory approach to get as much information as possible; and exploitation, in which the system uses the knowledge acquired during the warm-up to maximise the reward. For this two phases we combine different recommendation strategies, among which we consider both multi-armed bandits and classic algorithms. We evaluate them offline in three datasets: CM100K (music), MovieLens1M (films) and Twitter. We also study how the warm-up duration affects the exploitation phase. Results show that in two dataset (MovieLens and Twitter) classical algorithms perform better during the exploitation phase in terms of recall after a mainly exploratory warm-up phase

Bandidos multi-brazo en sistemas de recomendación

En los últimos años los sistemas de recomendación se han convertido en una herramienta fundamental ampliamente utilizada por gran cantidad de servicios muy diversos, tales como comercios electrónicos, redes sociales o plataformas de streaming. Este crecimiento se debe, en parte, a que los sistemas de recomendación proporcionan grandes ventajas tanto para el usuario, en tanto que mejoran su experiencia proporcionándole ideas sobre qué contenido consumir, como para los proveedores del servicio, ya que favorecen el consumo por parte de los usuarios y les ayudan a descubrir productos que de otra forma no habrían conocido. Este creciente auge de los sistemas de recomendación ha promovido el desarrollo de técnicas y herramientas software que persiguen la satisfacción por parte del usuario centrándose en mejorar las recomendaciones. Estas técnicas tienen un gran fundamento en el campo del Aprendizaje supervisado, puesto que la tarea de recomendación se ha entendido de manera tradicional como un problema de predicción. El hecho de tratar las recomendaciones como una predicción de la puntuación que el usuario daría a cada uno de los ítems que desconoce, supone entender la recomendación desde un punto de vista estático de un único paso en el que se busca el acierto inmediato sin tener en cuenta las consecuencias a largo plazo. Este trabajo aborda una nueva perspectiva en cuanto la recomendación que supone romper con este enfoque más tradicional: el Aprendizaje por Refuerzo. Mediante este nuevo enfoque, la recomendación pasa a entenderse como un proceso de aprendizaje continuo en el que las interacciones del usuario con las recomendaciones se aprovechan para adquirir nuevo conocimiento y mejorar a futuro. Por ello, se admite un sacrificio del acierto inmediato en pro de obtener un mayor beneficio a largo plazo. Esta idea de proceso interactivo entre usuario y sistema resulta mucho más natural que el enfoque estático más tradicional inspirado por el Aprendizaje supervisado. Más concretamente en este trabajo se profundiza en técnicas conocidas como bandidos multi-brazo, que tienen base en la teoría de la probabilidad, aplicadas a la tarea de la recomendación no personalizada. Dentro de los bandidos multi-brazo, se estudian los algoritmos de "-Greedy, Upper Confidence Bound y Thompson Sampling, comparándolos entre sí y con algoritmos clásicos de recomendación no personalizada en diferentes conjuntos de datos. Los resultados obtenidos indican que en todos los conjuntos de datos existen configuraciones de al menos uno de los algoritmos anteriores que mejoran el acierto con respecto a los algoritmos de recomendación sin refuerzo