SemRevRec: a recommender system based on user reviews and linked data

Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users' decision processes. In this paper we propose a novel recommendation approach based on the semantic annotation of entities mentioned in user reviews and on the knowledge available in the Web of Data. We compared our recommender system with two baseline algorithms and a state-of-the-art Linked Data based approach. Our system provided more diverse recommendations with respect to the other techniques considered, while obtaining a better accuracy than the Linked Data based method

Advances in session-based and session-aware recommendation

As of today, personalized item suggestions provided by an automated recommender system have become a crucial part of many online services, e.g., online shops or media streaming applications, and extensive evidence exists that such systems increase both the user experience as well as the revenue of the providers. In academia, the recommendation problem is often framed as finding suitable items that a user is not yet aware of based on his long-term preference profile. In the real world, however, this problem formulation has a number of problems. Long-term profiles, e.g., are not available for new or anonymous users and recommendations can then only be based on the few most recent interactions in an ongoing usage session. Various approaches to this highly relevant setting of session-based recommendation that recently emerged in the research community were proposed over the recent years. However, in terms of the evaluation procedure, no common standard has been established so far. In this thesis, the author, therefore, proposes a publicly available framework for reproducible research and, furthermore, fairly compares many approaches, of which some were proposed by himself. Extensive experiments and a user study surprisingly showed that comparably simple nearest-neighbor techniques usually outperform recent deep learning models across many domains, datasets, and metrics. Even if long-term preferences are available for the users, recent works indicated that it might still be beneficial to consider the ongoing session, e.g., because a user started the session with a specific intent in mind. The author of this thesis, thus, conducted a systematic statistical analysis to assess what helps recommendations in being effective in such a session-aware scenario. This analysis is based on log data from a fashion retailer and insights were, furthermore, operationalized into novel session-aware recommendation approaches. Matching items of the customer’s ongoing session, reminding him of previously inspected clothes, recommending discounted items, and considering recent trends in the community showed to be particularly effective strategies, not only for item-item recommendation but also in the related scenario of search personalization

FATREC Workshop on Responsible Recommendation Proceedings

We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible recommendation: ethical considerations in recommendation, bias and discrimination in recommender systems, transparency and accountability, social impact of recommenders, user privacy, and other related concerns. Our goal was to encourage the community to think about how we build and study recommender systems in a socially-responsible manner. Recommendation systems are increasingly impacting people\u27s decisions in different walks of life including commerce, employment, dating, health, education and governance. As the impact and scope of recommendations increase, developing systems that tackle issues of fairness, transparency and accountability becomes important. This workshop was held in the spirit of FATML (Fairness, Accountability, and Transparency in Machine Learning), DAT (Data and Algorithmic Transparency), and similar workshops in related communities. With Responsible Recommendation , we brought that conversation to RecSys

An analysis of popularity biases in recommender system evaluation and algorithms

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 03-10-2019Las tecnologías de recomendación han ido progresivamente extendiendo su presencia en las aplicaciones y servicios de uso diario. Los sistemas de recomendación buscan realizar sugerencias individualizadas de productos u opciones que los usuarios puedan encontrar interesantes o útiles. Implícita en el concepto de recomendación está la idea de que las sugerencias más satisfactorias para cada usuario son aquellas que tienen en cuenta sus gustos particulares, por lo que cabría esperar que los algoritmos de recomendación más eficaces sean los más personalizados. Sin embargo, se ha observado recientemente que recomendar simplemente los productos más populares no resulta una estrategia mucho peor que los mejores y más sofisticados algoritmos personalizados, y más aún, que estos tienden a sesgar sus recomendaciones hacia opciones mayoritarias. Por todo ello, es rele-vante entender en qué medida y bajo qué circunstancias es la popularidad una señal real-mente efectiva a la hora de recomendar, y si su aparente efectividad se debe a la existencia de ciertos sesgos en las metodologías de evaluación offline actuales, como todo parece indicar, o no. En esta tesis abordamos esta cuestión desde un punto de vista plenamente formal, identificando los factores que pueden determinar la respuesta y modelizándolos en térmi-nos de dependencias probabilísticas entre variables aleatorias, tales como la votación, el descubrimiento y la relevancia. De esta forma, caracterizamos situaciones concretas que garantizan que la popularidad sea efectiva o que no lo sea, y establecemos las condiciones bajo las cuales pueden existir contradicciones entre el acierto observado y el real. Las principales conclusiones hacen referencia a escenarios simplificados prototípicos, más allá de los cuales el análisis formal concluye que cualquier resultado es posible. Para profun-dizar en el escenario general sin suposiciones tan simplificadas, estudiamos un caso parti-cular donde el descubrimiento de ítems es consecuencia de la interacción entre usuarios en una red social. Además, en esta tesis proporcionamos una explicación formal del sesgo de populari-dad que presentan los algoritmos de filtrado colaborativo. Para ello, desarrollamos una versión probabilística del algoritmo de vecinos próximos kNN. Dicha versión evidencia además la condición fundamental que hace que kNN produzca recomendaciones perso-nalizadas y se diferencie de la popularidad pura

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

A hybrid approach for item collection recommendations : an application to automatic playlist continuation

Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version

Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen