Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

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

Predictive statistical user models under the collaborative approach

Mención Internacional en el título de doctorUser models and recommender systems due to their similarity can be considered the same thing except from the use that we make of them. Both have their root in multiple disciplines such as information retrieval or machine learning among others. The impact has grown rapidly with the importance of data on systems and applications. Most of the big companies employ one of the other for different reasons such as: gathering more customers, boost sales or increase revenue. Thus very well-known companies like Amazon, EBay or Google use models to improve their businesses. In fact, as data becomes more and more important for companies, universities and people, user models are crucial to make decisions over large amounts of data. Although user models can provide accurate predictions on large populations their use and application is not restricted to predictions but can be extended to selection of dialogue strategies or detection of communities within complex domains. After a deep review of the existing literature, it was found that there is a lack of statistical user models based on experience plus the existing models in the area are content-based models that suffer from major problems as scalability, cold-start or new user problem. Furthermore, researchers in the area of user modelling usually develop their own models and then perform ad-hoc evaluations that are not replicable and therefore not comparable. The lack of a complete framework for evaluation makes very difficult to compare results across models and domains. There are two main approaches to build a user model or recommender system: the content based approach, where predictions are based on the same user past behaviours; and the collaborative approach where predictions rely on like-minded people. Both approaches have advantages but also downsides that have to be considered before building a model. The main goal of this thesis is to develop a hybrid user model that takes the strengths of both approaches and mitigates the downsides by combining both methods. The proposed hybrid model is based on an R-Tree structure. The selection of this structure to support the models is backed from the fact that the rectangle tree is specifically designed to effectively store and manipulate multidimensional data. This data structure introduced by Guttman in 1984 is a height balanced tree that only requires visiting a few nodes to perform a tree search. As a result, it can manage large populations of data efficiently as only a few nodes are visited during the inference. R-Tree has two different typologies of nodes: the leaf-node and the non-leaf node. Leaf nodes contain the whole universe of users while non leaf nodes are somehow redundant and contain summaries of child nodes. Along this thesis two statistical user models based on experience have been proposed. The first one is a knowledge base user mode (KLUM), is a classical approach that summarizes and remove data in order to keep performance level within reasonable margins. The second one, an R-Tree user model (RTUM), is an innovative model based on an R-Tree structure. This new model not only solves the problem of removing data but also the scalability problem which turns out to be one of the major problems in the area of user modelling. Both models have been developed and tested with equivalent formulations to make comparisons relevant. Both models are prepared to create their own knowledge base from scratch but also they can be fed with expert knowledge. Thus alleviating another major problem in the area of user modelling as it is the start-up problem. Regarding the proposal of this thesis, two statistical user models are proposed (KLUM and RTUM). In addition, a refinement of RTUM user model is proposed, while RTUM performs node partitions based on the centroids of the users in that node, the new refinement implements a new partition based on privileged features. Hence, the new approach takes advantage of most discriminatory features of the domain to perform the partition. This new approach not only provides accurate inferences, but also an excellent clustering that can be useful in many different scenarios. For instance, this clustering can be employed in the area of social networks to detect communities within the social network. This is a tough task that has been one of the goals of many researchers during the last few years. This thesis also provides a complete evaluation of the models with a great diversity of parameterizations and domains. The models are tested in four different domains and as a result of the evaluation, it is proved that RTUM user model provides a massive gain against classical user models as KLUM. During the evaluation, RTUM reached success rates of 85% while the analogous KLUM could only reach a 65% thus leaving a 20% gain for the proposed model. The evaluation provided not only compares models and success rates, but also provides a broad analysis of how every parameter of the models impact the performance plus a complete study of the databases sizes and inference times for the models. The main conclusion to the evaluation is that after a complete evaluation with a wide diversity of parameters and domains RTUM outperforms KLUM on every scenario tested. As previously mentioned, after the literature review it was also found a lack of evaluation frameworks for user modelling. This thesis also provides a complete evaluation framework for user modelling. This fills a gap in the literature as well as makes the evaluation replicable and therefore comparable. Along years researchers and developers had found difficulties to compare evaluations and measure the quality of their models in different domains due to the lack of an evaluation standard. The evaluation framework presented in this thesis covers data samples including training set and test set plus different sets of experiments alongside with a statistical analysis of the domain, confidence intervals and confidence levels to guarantee that each experiment is statistically significant. The evaluation framework can be downloaded and then used to complete evaluations and cross-validate results across different models.This thesis would not have been possible without the financial support of the following research projects Cadooh (TSI-020302-2011-21), Thuban (TIN2008-02711) that funded part of this research.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Antonio de Amescua Seco.- Secretario: Ruth Cobos Pérez.- Vocal: Dominikus Heckman

Context-Based Cultural Visits

Over the last two decades, there have been tremendous advances in mobile technologies, which have increased the interest in studying and developing mobile augmented reality systems, especially in the field of Cultural Heritage. Nowadays, people rely even more on smartphones, for example, when visiting a new city to search for information about monuments and landmarks, and the visitor expects precise and tailored information to his needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places around the city, incorporating contextual information about the visitor and his needs. This document presents a novel mobile augmented reality application, NearHeritage, that was developed within the scope of the master's thesis on Electrical and Computers Engineering from the Faculty of Engineering of Porto University (FEUP), in collaboration with INESC TEC. The research carried out was focused on the importance of utilising modern technologies to assist the visitors in finding and exploring Cultural Heritage. In this way, it is provided not only the nearby points-of-interest of a city but also detailed information about each POI. The solution presented uses built-in sensors and hardware of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing) to retrieve information about the landmarks and the visitor context. Also, these are crucial hardware components for implementing the full potential of augmented reality tools to create innovative contents that increase the overall user experience. All the experiments were conducted in Porto, Portugal, and the final results showcase that the concept of a MAR application can improve the user experience in discovering and learning more about Cultural Heritage around the world, creating an interactive, enjoyable and unforgettable adventure