537 research outputs found

    Rating Prediction based on Optimal Review Topics: A Proposed Latent Factors-Optimal Topics Hybrid Approach

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    Rating prediction is an inevitable problem which recommender systems (RS) need to address. Its goal is to accurately predict the rating a user will assign to a particular item. Predictions which utilize numerical ratings and review texts are biased and have low accuracy. Also, existing topic-based rating prediction approaches focus on finding the most preferred items through the identification of latent topics expressed in users’ review texts. Even though the latent topics seem to represent most user review texts, they do not necessarily capture each user’s preferences. The goal of this work is then to develop a more accurate model by considering product review texts analysis so as to gain additional preference knowledge. Hence, a hybrid algorithm that optimizes the latent topics is proposed.  Specifically, the proposed approach finds appropriate weights for the topics of each review text. Rating prediction is critical task for RS because slight performance enhancement of the prediction accuracy results into significant improvements in recommendations. Experimental evaluation over real-world datasets revealed performance improvements of the proposed approach compared to alternative models. The proposed model can be used by RS in various domain such as e-learning, movie and hotel rating

    A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

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    E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

    Modeling Dynamic User Interests: A Neural Matrix Factorization Approach

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    In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods

    Determinants of online leisure travel planning decision processes :a segmented approach

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    D.B.A. ThesisThere is an abundance of information sources on the Internet that consumers use to plan and book their travel. This information reflects the fact that travel comprises a significant part of the business conducted through the web. Consumers are sometimes faced with a complex task of making purchasing decisions in the dynamic and fast-paced medium of the Internet. In spite of the importance of travel and the intricacies of the decision process, an integrated framework that identifies the various determinants of the online leisure travel planning decision process and how they interact, is largely absent in travel literature. This study aims to make a contribution by extracting from relevant literature useful elements that could comprise such a framework. It also uses several phases of qualitative research to refine the framework, and then a quantitative assessment of data collected from an online questionnaire completed by 1,198 respondents to test specific components of the framework that deal with online travel booking intention. In the final model building stage, three logistic regression models were compared. The first is a parsimonious one containing key determinants that lead to online travel booking intention. These determinants emerged from theoretical frameworks of the theory of reasoned action and innovation adoption theory. The second Model used strictly involvement, motivation, and knowledge variables that are thought to influence online booking intention. The third Model included a combination of relevant predictor variables from the other two Models. The relationship between various demographics and online travel booking intention was investigated yielding some interesting insights. Consequently, this study recommends these demographic variables be considered in segmenting travelers to find those more likely to book online. The determinants of online leisure travel booking decision processes could be used in conjunction with demographic variables to more accurately predict leisure travel website usage

    Big Data and Artificial Intelligence in Digital Finance

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    This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

    Neural recommender models for sparse and skewed behavioral data

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    Modern online platforms offer recommendations and personalized search and services to a large and diverse user base while still aiming to acquaint users with the broader community on the platform. Prior work backed by large volumes of user data has shown that user retention is reliant on catering to their specific eccentric tastes, in addition to providing them popular services or content on the platform. Long-tailed distributions are a fundamental characteristic of human activity, owing to the bursty nature of human attention. As a result, we often observe skew in data facets that involve human interaction. While there are superficial similarities to Zipf's law in textual data and other domains, the challenges with user data extend further. Individual words may have skewed frequencies in the corpus, but the long-tail words by themselves do not significantly impact downstream text-mining tasks. On the contrary, while sparse users (a majority on most online platforms) contribute little to the training data, they are equally crucial at inference time. Perhaps more so, since they are likely to churn. In this thesis, we study platforms and applications that elicit user participation in rich social settings incorporating user-generated content, user-user interaction, and other modalities of user participation and data generation. For instance, users on the Yelp review platform participate in a follower-followee network and also create and interact with review text (two modalities of user data). Similarly, community question-answer (CQA) platforms incorporate user interaction and collaboratively authored content over diverse domains and discussion threads. Since user participation is multimodal, we develop generalizable abstractions beyond any single data modality. Specifically, we aim to address the distributional mismatch that occurs with user data independent of dataset specifics; While a minority of the users generates most training samples, it is insufficient only to learn the preferences of this subset of users. As a result, the data's overall skew and individual users' sparsity are closely interlinked: sparse users with uncommon preferences are under-represented. Thus, we propose to treat these problems jointly with a skew-aware grouping mechanism that iteratively sharpens the identification of preference groups within the user population. As a result, we improve user characterization; content recommendation and activity prediction (+6-22% AUC, +6-43% AUC, +12-25% RMSE over state-of-the-art baselines), primarily for users with sparse activity. The size of the item or content inventories compounds the skew problem. Recommendation models can achieve very high aggregate performance while recommending only a tiny proportion of the inventory (as little as 5%) to users. We propose a data-driven solution guided by the aggregate co-occurrence information across items in the dataset. We specifically note that different co-occurrences are not equally significant; For example, some co-occurring items are easily substituted while others are not. We develop a self-supervised learning framework where the aggregate co-occurrences guide the recommendation problem while providing room to learn these variations among the item associations. As a result, we improve coverage to ~100% (up from 5%) of the inventory and increase long-tail item recall up to 25%. We also note that the skew and sparsity problems repeat across data modalities. For instance, social interactions and review content both exhibit aggregate skew, although individual users who actively generate reviews may not participate socially and vice-versa. It is necessary to differentially weight and merge different data sources for each user towards inference tasks in such cases. We show that the problem is inherently adversarial since the user participation modalities compete to describe a user accurately. We develop a framework to unify these representations while algorithmically tackling mode collapse, a well-known pitfall with adversarial models. A more challenging but important instantiation of sparsity is the few-shot setting or cross-domain setting. We may only have a single or a few interactions for users or items in the sparse domains or partitions. We show that contextualizing user-item interactions helps us infer behavioral invariants in the dense domain, allowing us to correlate sparse participants to their active counterparts (resulting in 3x faster training, ~19% recall gains in multi-domain settings). Finally, we consider the multi-task setting, where the platform incorporates multiple distinct recommendations and prediction tasks for each user. A single-user representation is insufficient for users who exhibit different preferences along each dimension. At the same time, it is counter-productive to handle correlated prediction or inference tasks in isolation. We develop a multi-faceted representation approach grounded on residual learning with heterogeneous knowledge graph representations, which provides us an expressive data representation for specialized domains and applications with multimodal user data. We achieve knowledge sharing by unifying task-independent and task-specific representations of each entity with a unified knowledge graph framework. In each chapter, we also discuss and demonstrate how the proposed frameworks directly incorporate a wide range of gradient-optimizable recommendation and behavior models, maximizing their applicability and pertinence to user-centered inference tasks and platforms

    Big Data and Artificial Intelligence in Digital Finance

    Get PDF
    This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance
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