Toward Education 3.0: Pedagogical Affordances and Implications of Social Software and the Semantic Web

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116348/1/tl20167.pd

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

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

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

Personalized information retrieval in digital ecosystems

Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users? search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting theweight of category which represents users? search preference.By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match users? information needs

Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation

Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information. In this work, we focus on the more general Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is challenging because there are no overlapped entities (e.g., users and items) between domains, and there is only users' implicit feedback and no content information. Previous CR methods cannot solve NCSR well, since (1) they either need extra content to align domains or need explicit domain alignment constraints to reduce the domain discrepancy from domain-invariant features, (2) they pay more attention to users' explicit feedback (i.e., users' rating data) and cannot well capture their sequential interaction patterns, (3) they usually do a single-target cross-domain recommendation task and seldom investigate the dual-target ones. Considering the above challenges, we propose Prompt Learning-based Cross-domain Recommender (PLCR), an automated prompting-based recommendation framework for the NCSR task. Specifically, to address the challenge (1), PLCR resorts to learning domain-invariant and domain-specific representations via its prompt learning component, where the domain alignment constraint is discarded. For challenges (2) and (3), PLCR introduces a pre-trained sequence encoder to learn users' sequential interaction patterns, and conducts a dual-learning target with a separation constraint to enhance recommendations in both domains. Our empirical study on two sub-collections of Amazon demonstrates the advance of PLCR compared with some related SOTA methods

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