Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201

Rating and aspect-based opinion graph embeddings for explainable recommendations

The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent recommendation methods based on graph embeddings have shown state-of-the-art performance. In general, these methods encode latent rating patterns and content features. Differently from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Additionally, our method has the advantage of providing explanations that involve the coverage of aspect-based opinions given by users about recommended items.Comment: arXiv admin note: substantial text overlap with arXiv:2107.0322

Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality

Matrix factorization (MF) is extensively used to mine the user preference from explicit ratings in recommender systems. However, the reliability of explicit ratings is not always consistent, because many factors may affect the user's final evaluation on an item, including commercial advertising and a friend's recommendation. Therefore, mining the reliable ratings of user is critical to further improve the performance of the recommender system. In this work, we analyze the deviation degree of each rating in overall rating distribution of user and item, and propose the notion of user-based rating centrality and item-based rating centrality, respectively. Moreover, based on the rating centrality, we measure the reliability of each user rating and provide an optimized matrix factorization recommendation algorithm. Experimental results on two popular recommendation datasets reveal that our method gets better performance compared with other matrix factorization recommendation algorithms, especially on sparse datasets

Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations

The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, current recommendation methods based on graph embeddings have shown state-of-the-art performance. These methods commonly encode latent rating patterns and content features. Different from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Our approach has the advantage of providing explanations which leverage aspect-based opinions given by users about recommended items. Furthermore, we also provide examples of the applicability of recommendations utilizing aspect opinions as explanations in a visualization dashboard, which allows obtaining information about the most and least liked aspects of similar users obtained from the embeddings of an input graph

Poisson Matrix Factorization For TV Recommendations

Recommendation systems are becoming more and more popular within e-commerce websites to help drive user engagement. It is not just limited to e-commerce though, websites such as Netflix or Spotify utilize recommendation systems to better engage users in movies and TV shows, or music. This thesis explores the mathematics and assumptions behind recommendation systems, such as how data is distributed and different algorithms used. The thesis then performs a case study on Reddit TV show data to build a recommendation system. To improve the results of the recommendation system, this thesis makes changes to a Python Recommendation System Library to enable Poisson Factorization. The changes proposed can be integrated into the existing Python library, helping other programmers make more meaningful and accurate recommendations

Evaluating Collaborative Filtering Algorithms for Music Recommendations on Chinese Music Data

In this thesis, I explored Collaborative Filtering algorithms used in music recommendation tasks in the Music Information Retrieval field. To find out if those CF algorithms work on Chinese music data, I developed a new dataset from the mainstream Chinese music streaming platform NetEase Could Music, and compared the performance of a series of Memory-based and Model-based collaborative filtering algorithms on our dataset. Our experimental results prove that these CF algorithms aiming at users’ information are effective on our dataset, and they have the predictive ability of music recommendation tasks on Chinese music data. In general, Model-based algorithms perform better than Memory-based algorithms. Within them, the SVD++ algorithm from Matrix Factorization-based methods reaches the best overall accuracy.Bachelor of Scienc

Diseño de un sistema de recomendación usando algoritmos de aprendizaje máquina

Este trabajo está constituido por una sección inicial donde se presenta una breve introducción a la problemática de los sistemas de recomendación susceptibles a cold-start y pobre eficiencia ante el procesamiento de matrices dispersas. El objetivo principal de este trabajo es proponer y desarrollar un sistema de recomendación que incorpore contenido para solventar el problema del cold-start en los métodos de filtrado colaborativo, a la vez que permita una formulación e implementación con matrices dispersas para un procesamiento eficiente. En la sección de antecedentes se abordan los primeros enfoques de los sistemas de recomendación basados en filtrado y distribución de la información. También se incluye una revisión de los primeros modelos de filtrado colaborativo y una pequeña reseña de los sistemas de recomendación usados en algunas empresas. Se presenta un capítulo de marco teórico donde se explican los algoritmos para extracción de características, las formulaciones de sistemas de recomendación utilizados y la forma en que se calculan algunas de las métricas más usadas para evaluar los sistemas de recomendación. Se presenta el desarrollo del trabajo donde se explica a detalle cada paso realizado para la formulación e implementación de un sistema compuesto que integra dos enfoques, uno de filtrado colaborativo SVD y uno de contenido. Posteriormente se muestran los resultados de cada experimento realizado para validar el sistema de recomendación propuesto. Finalmente se presentan las conclusiones del trabajo. Los resultados demuestran que se resuelven de manera particular los objetivos de procesamiento de datos no estructurados y se aborda el problema del cold-start incorporando el contenido derivado de los datos no estructurados. Con el uso de un enfoque de matrices dispersas, la implementación resultante es modular y escalable.ITESO, A. C.Consejo Nacional de Ciencia y TecnologíaUnima Diagnóstico

Sistemas recomendadores aplicados en Educación

En este trabajo final integrador se analizaron diferentes técnicas de recomendación y se estudió su aplicabilidad en el ámbito educativo. Así también se presenta un resumen de las métricas usualmente utilizadas para medir la performance de éstos sistemas y cuáles son las variantes o nuevas métricas a tener en cuenta cuando se aplican éstos sistemas en educación. En el trabajo experimental se utilizaron diferentes conjuntos de datos de prueba abordados en la literatura de los SRE y se compararon los resultados obtenidos con distintos algoritmos de recomendación basados en la técnica de Filtrado Colaborativo (FC).Facultad de Informátic