Modelling and analysis of temporal preference drifts using a component-based factorised latent approach

In recommender systems, human preferences are identified by a number of individual components with complicated interactions and properties. Recently, the dynamicity of preferences has been the focus of several studies. The changes in user preferences can originate from substantial reasons, like personality shift, or transient and circumstantial ones, like seasonal changes in item popularities. Disregarding these temporal drifts in modelling user preferences can result in unhelpful recommendations. Moreover, different temporal patterns can be associated with various preference domains, and preference components and their combinations. These components comprise preferences over features, preferences over feature values, conditional dependencies between features, socially-influenced preferences, and bias. For example, in the movies domain, the user can change his rating behaviour (bias shift), her preference for genre over language (feature preference shift), or start favouring drama over comedy (feature value preference shift). In this paper, we first propose a novel latent factor model to capture the domain-dependent component-specific temporal patterns in preferences. The component-based approach followed in modelling the aspects of preferences and their temporal effects enables us to arbitrarily switch components on and off. We evaluate the proposed method on three popular recommendation datasets and show that it significantly outperforms the most accurate state-of-the-art static models. The experiments also demonstrate the greater robustness and stability of the proposed dynamic model in comparison with the most successful models to date. We also analyse the temporal behaviour of different preference components and their combinations and show that the dynamic behaviour of preference components is highly dependent on the preference dataset and domain. Therefore, the results also highlight the importance of modelling temporal effects but also underline the advantages of a component-based architecture that is better suited to capture domain-specific balances in the contributions of the aspects

On conditional random fields: applications, feature selection, parameter estimation and hierarchical modelling

There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, which is a combination of graph theory and probability theory. This thesis focuses on a special type of graphical models known as Conditional Random Fields (CRFs) (Lafferty et al., 2001), in which the output state spaces, when conditioned on some observational input data, are represented by undirected graphical models. The contributions of thesis involve both (a) broadening the current applicability of CRFs in the real world and (b) deepening the understanding of theoretical aspects of CRFs. On the application side, we empirically investigate the applications of CRFs in two real world settings. The first application is on a novel domain of Vietnamese accent restoration, in which we need to restore accents of an accent-less Vietnamese sentence. Experiments on half a million sentences of news articles show that the CRF-based approach is highly accurate. In the second application, we develop a new CRF-based movie recommendation system called Preference Network (PN). The PN jointly integrates various sources of domain knowledge into a large and densely connected Markov network. We obtained competitive results against well-established methods in the recommendation field.On the theory side, the thesis addresses three important theoretical issues of CRFs: feature selection, parameter estimation and modelling recursive sequential data. These issues are all addressed under a general setting of partial supervision in that training labels are not fully available. For feature selection, we introduce a novel learning algorithm called AdaBoost.CRF that incrementally selects features out of a large feature pool as learning proceeds. AdaBoost.CRF is an extension of the standard boosting methodology to structured and partially observed data. We demonstrate that the AdaBoost.CRF is able to eliminate irrelevant features and as a result, returns a very compact feature set without significant loss of accuracy. Parameter estimation of CRFs is generally intractable in arbitrary network structures. This thesis contributes to this area by proposing a learning method called AdaBoost.MRF (which stands for AdaBoosted Markov Random Forests). As learning proceeds AdaBoost.MRF incrementally builds a tree ensemble (a forest) that cover the original network by selecting the best spanning tree at a time. As a result, we can approximately learn many rich classes of CRFs in linear time. The third theoretical work is on modelling recursive, sequential data in that each level of resolution is a Markov sequence, where each state in the sequence is also a Markov sequence at the finer grain. One of the key contributions of this thesis is Hierarchical Conditional Random Fields (HCRF), which is an extension to the currently popular sequential CRF and the recent semi-Markov CRF (Sarawagi and Cohen, 2004). Unlike previous CRF work, the HCRF does not assume any fixed graphical structures.Rather, it treats structure as an uncertain aspect and it can estimate the structure automatically from the data. The HCRF is motivated by Hierarchical Hidden Markov Model (HHMM) (Fine et al., 1998). Importantly, the thesis shows that the HHMM is a special case of HCRF with slight modification, and the semi-Markov CRF is essentially a flat version of the HCRF. Central to our contribution in HCRF is a polynomial-time algorithm based on the Asymmetric Inside Outside (AIO) family developed in (Bui et al., 2004) for learning and inference. Another important contribution is to extend the AIO family to address learning with missing data and inference under partially observed labels. We also derive methods to deal with practical concerns associated with the AIO family, including numerical overflow and cubic-time complexity. Finally, we demonstrate good performance of HCRF against rivals on two applications: indoor video surveillance and noun-phrase chunking

Exploiting subsequence matching in Recommender Systems

Desde su surgimiento al inicio de la década de los 90, los sistemas de recomendación han experimentado un crecimiento exponencial empleándose en cada vez más aplicaciones debido a la utilidad que tienen para ayudar a los usuarios a elegir artículos en función de sus gustos y necesidades. Actualmente son indispensables en un gran número de empresas que ofrecen su servicio a través de Internet, el medio de intercambio de información más importante que existe. Por esta razón, la continua innovación en estos sistemas resulta imprescindible para poder efectuar recomendaciones que sean capaces de seguir sorprendiendo a los usuarios y mejorar las ya existentes. En este Trabajo Fin de Máster hemos realizado un estudio e investigación acerca del estado actual de estos sistemas, prestando especial atención a los sistemas de filtrado colaborativo basados en vecinos y los basados en contenido. No obstante, debido a las desventajas que puede tener cada sistema por separado normalmente en aplicaciones reales se emplean combinaciones de varios sistemas, creando recomendadores híbridos. Como apoyo a este estudio, se propone como aspecto novedoso el uso del algoritmo de la subcadena común más larga (LCS) para ser utilizada como medida de similitud entre usuarios, introduciendo además, una técnica general y transparente para generar secuencias haciendo uso tanto de información de contenido como de información colaborativa, pudiendo generar recomendadores híbridos de manera sencilla. Complementando a estos nuevos recomendadores, también detallamos otros parámetros auxiliares (confianza, preferencia, normalizaciones y distintas ordenaciones) que tienen como fin mejorar el rendimiento de estos sistemas basados en LCS. Por otro lado, además de la definición de estos nuevos recomendadores, el trabajo se complementa con resultados experimentales haciendo uso de tres conjuntos de datos conocidos en el área: Movielens, Lastfm y MovieTweetings. Cada uno de ellos estará orientado a explotar un aspecto específico de la generación de secuencias. Los resultados han sido obtenidos haciendo uso de métricas de ranking (Precisión, Recall, MAP o nDCG) y de novedad y diversidad (_-nDCG, EPC, EPD, Aggregate diversity, EILD y Gini). Los resultados han tenido como fin comparar el rendimiento de los recomendadores basados en la subsecuencia común más larga frente a otros recomendadores conocidos en el área. Como resumen, se ha observado que los recomendadores propuestos resultan altamente competitivos en las pruebas realizadas siendo incluso mejores en algunas ocasiones a otros recomendadores conocidos en el área, tanto en términos de métricas de ranking como de novedad y diversidad. No obstante, también se ha observado que, en algunos casos, el uso de recomendadores híbridos basados en la subsecuencia común más larga obtiene unos resultados peores que otras versiones puramente colaborativas. En cualquier caso, consideramos que es una propuesta con potencial para seguir siendo investigada.Since their inception in the early 1990s, recommender systems have experienced exponential growth as they are being used in a large number of applications because of their usefulness in helping users choose items based on their tastes and needs. Nowadays, they are indispensable in many companies that o er their service through the Internet, the most important method for information exchange. For this reason, continuous innovation in these systems is essential to make recommendations that are able to continue surprising users, while improving the existing ones. In this Master's Thesis, we have studied and researched on the current state of these systems, paying special attention to collaborative ltering based on neighborhood and content-based algorithms. However, due to the disadvantages that each system may have separately, combinations of these systems are often used in real applications, creating hybrid recommenders. To support this study, we propose the use of the longest common subsequence (LCS) algorithm as a novel aspect to be used as a similarity metric between users, also introducing a general and transparent technique to generate sequences using both content and collaborative information, allowing us to generate hybrid recommenders in a simple way. Complementing these new recommendations, we also detail other auxiliary parameters (con dence, preference, normalization functions, and di erent orderings), whose main goal is to improve the performance of these LCS-based systems. On the other hand, in addition to the de nition of these new recommenders, the study is complemented with experimental results using three well-known datasets in the area: Movielens, Lastfm and MovieTweetings. Each one of them will be oriented to exploit a speci c aspect of the sequence generation process. The results have been obtained by using ranking metrics (Precision, Recall, MAP, or nDCG) and novelty and diversity metrics ( -nDCG, EPC, EPD, Aggregate diversity, EILD, and Gini). With these experiments, we aimed at comparing the performance of recommenders based on the longest common subsequence against other well-known recommenders in the area. As a summary, we have observed in the experiments performed that the proposed recommenders are highly competitive, and sometimes they are even better than other recommenders known in the area, both in terms of ranking quality metrics, and novelty and diversity dimensions. However, we have also observed that, in some cases, the use of hybrid recommenders based on the longest common subsequence results in worse performance than other purely collaborative versions. In any case, we believe this is a proposal with enough potential to be worthy of further investigation

A framework for leveraging properties of user reviews in recommendation

With the growing volume of information online, it is increasingly harder for users to identify useful information to support their choices when interacting with different items. Review-based recommendation systems, which leverage reviews posted by users on items to estimate the users’ preferences, have been shown to be a credible solution for addressing the problem of identifying their preferred items. However, the actual usefulness of these reviews impact the effectiveness of the resulting recommender systems, especially with the enormous volume of available reviews online. In particular, as argued by the widely cited users’ adoption of information framework, users exhibit distinct preferences for reviews depending on the properties of these reviews (e.g. length, sentiment) when making decisions. Therefore, we argue that not all reviews are equally useful for different users. We aim to effectively modelling the personalised usefulness of reviews through the use of reviews’ properties when developing review-based recommendation techniques. Note that, few studies in the literature investigated the effectiveness of leveraging the properties of reviews to develop effective review-based recommendation approaches. This thesis aims to address this research gap by proposing a review-based recommendation framework. Such a framework models the personalised usefulness of reviews according to various reviews’ properties, including the reviews’ age, length, sentiment, ratings, helpfulness as judged by the users and helpfulness as predicted by a review helpfulness classifier. In particular, the thesis addresses two main challenges: (i) the availability of the attributes of reviews and (ii) the users’ preferences estimation. The first challenge refers to the difficulty of extracting particular review properties from their corresponding attributes. For example, extraction of the age property relies on the availability of the timestamps of the corresponding reviews. We address the availability of the reviews’ attributes to extract their sentiment and helpfulness properties with classification techniques. The sentiment property of reviews is estimated through effective state-of-the-art sentiment classifiers. We first evaluate the estimated reviews’ sentiment in comparison to the users’ ratings in typical recommendation approaches. Then, we introduce a sentiment attention mechanism to encode the estimated reviews’ sentiment. Our experiments show that the sentiment property can effectively replace the users’ ratings when estimating the user preferences. Moreover, by leveraging the estimated sentiment property of reviews, our proposed review-based rating prediction model shows improved performance compared to state-of-the-art rating prediction models. Next, the extraction of the reviews’ helpfulness property leverages the reviews’ helpful votes (i.e. a type of feedback given by other reviewers providing information on whether the corresponding review is helpful to them). However, the number of helpful votes for each review are not commonly available. In particular, we propose a novel weakly supervised review helpfulness classification correction approach (i.e. the Negative Confidence-aware Weakly Supervised (NCWS) approach), which leverages the confidence in a given review being unhelpful with respect to its age. We experimentally show that NCWS-based classifiers significantly outperform existing review helpfulness classifiers on two public review datasets. Moreover, the estimated helpfulness of reviews by NCWS-based classifiers can significantly improve the performance of a review-based rating prediction model. Next, to address our second challenge pertaining to the users’ preferences estimation, we aim to estimate their preferences when using reviews exhibiting different properties to accurately predict their preferred items. In particular, we propose two novel ranking-based recommendation approaches (named RPRM and BanditProp), which models the users’ preferences using different review properties with different techniques. The RPRM model applies the attention mechanism to model the usefulness of reviews according to different review properties. Unlike RPRM, the BanditProp model leverages existing bandit algorithms and introduces a novel contextual bandit algorithm to tackle the users’ preference estimation of using specific reviews’ properties to identify useful reviews. Our experiments show that RPRM can outperform stateof-the-art review-based recommendation models, and BanditProp can significantly outperform RPRM on two publicly available review datasets. These results validate the effectiveness of leveraging the review properties when examining the usefulness of reviews to improve the performances of review-based recommendation techniques. Overall, we contribute an effective review-based recommendation framework that make accurate recommendations by leveraging the reviews’ associated properties. This framework includes models for extracting properties from reviews, and various techniques that are required to integrate the learned properties, which, in turn and according to our conducted experiments, provide good approximations of a given users’ item preferences. These contributions make progress in the development of review-based recommendation techniques and could inspire future directions of research in recommendation systems

Recommender system performance evaluation and prediction: information retrieval perspective

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201

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