294 research outputs found

    A Recommender System for Telecom Users: Experimental Evaluation of Recommendation Algorithms

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    The increasing flourish of available services in telecom domain offers more choices to the end user. On the other hand, such wide offer cannot be completely evaluated by the user, and some services may pass unobserved even if useful. To face this issue, the usage of recommendation systems in telecom domain is growing, to directly notify the user about the presence of services which may meet user interests. Recommendation can be seen as an advanced form of personalization, because user preferences are used to predict the interests of users for a new service. In this paper we propose a recommender system for users of telecom services, based on different collaborative filtering algorithms applied to a complex data-set of telecom users. Experiments on the recommendation performance and accuracy are conducted to test the different effects of different algorithms on a data set coming from the telecom domain

    A 3D Visual Interface for Critiquing-based Recommenders: Architecture and Interaction

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    Nowadays e-commerce websites offer users such a huge amount of products, which far from facilitating the buying process, actually make it more difficult. Hence, recommenders, which learn from users' preferences, are consolidating as valuable instruments to enhance the buying process in the 2D Web. Indeed, 3D virtual environments are an alternative interface for recommenders. They provide the user with an immersive 3D social experience, enabling a richer visualisation and increasing the interaction possibilities with other users and with the recommender. In this paper, we focus on a novel framework to tightly integrate interactive recommendation systems in a 3D virtual environment. Specifically, we propose to integrate a Collaborative Conversational Recommender (CCR) in a 3D social virtual world. Our CCR Framework defines three layers: the user interaction layer (3D Collaborative Space Client), the communication layer (3D Collaborative Space Server), and the recommendation layer (Collaborative Conversational Recommender). Additionally, we evaluate the framework based on several usability criteria such as learnability, perceived efficiency and effectiveness. Results demonstrate that users positively valued the experience

    A Cognitively Inspired Clustering Approach for Critique-Based Recommenders

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    The purpose of recommender systems is to support humans in the purchasing decision-making process. Decision-making is a human activity based on cognitive information. In the field of recommender systems, critiquing has been widely applied as an effective approach for obtaining users' feedback on recommended products. In the last decade, there have been a large number of proposals in the field of critique-based recommenders. These proposals mainly differ in two aspects: in the source of data and in how it is mined to provide the user with recommendations. To date, no approach has mined data using an adaptive clustering algorithm to increase the recommender's performance. In this paper, we describe how we added a clustering process to a critique-based recommender, thereby adapting the recommendation process and how we defined a cognitive user preference model based on the preferences (i.e., defined by critiques) received by the user. We have developed several proposals based on clustering, whose acronyms are MCP, CUM, CUM-I, and HGR-CUM-I. We compare our proposals with two well-known state-of-the-art approaches: incremental critiquing (IC) and history-guided recommendation (HGR). The results of our experiments showed that using clustering in a critique-based recommender leads to an improvement in their recommendation efficiency, since all the proposals outperform the baseline IC algorithm. Moreover, the performance of the best proposal, HGR-CUM-I, is significantly superior to both the IC and HGR algorithms. Our results indicate that introducing clustering into the critique-based recommender is an appealing option since it enhances overall efficiency, especially with a large data set

    TOPSIS for mobile based group and personal decision support system

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    Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS

    Evaluating Conversational Recommender Systems: A Landscape of Research

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    Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices

    Recopilación y recomendación de objetos de aprendizaje

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    En este trabajo se presenta una línea de investigación orientada hacia el desarrollo de distintas herramientas informáticas que ayuden a la gestión de repositorios de objetos de aprendizaje. En este sentido se trabaja en dos aspectos fundamentales para mejorar su usabilidad. Un aspecto es dar soporte a las tareas de recopilación de documentos que realiza el administrador del repositorio con el objetivo de detectar documentos plausibles de ser cargados en estos repositorios juntos con sus metadatos de interés. El otro aspecto es continuar trabajando en la recomendación de objetos de aprendizaje considerando no sólo el perfil del usuario y el diseño instruccional del material educativo sino también la valoración colaborativa de grupos de estudiantes similares.Eje: Tecnología Informática Aplicada en EducaciónRed de Universidades con Carreras en Informática (RedUNCI

    Leveraging Large Language Models in Conversational Recommender Systems

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    A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common-sense reasoning into language understanding, unlocking the potential of this paradigm. However, effectively leveraging LLMs within a CRS introduces new technical challenges, including properly understanding and controlling a complex conversation and retrieving from external sources of information. These issues are exacerbated by a large, evolving item corpus and a lack of conversational data for training. In this paper, we provide a roadmap for building an end-to-end large-scale CRS using LLMs. In particular, we propose new implementations for user preference understanding, flexible dialogue management and explainable recommendations as part of an integrated architecture powered by LLMs. For improved personalization, we describe how an LLM can consume interpretable natural language user profiles and use them to modulate session-level context. To overcome conversational data limitations in the absence of an existing production CRS, we propose techniques for building a controllable LLM-based user simulator to generate synthetic conversations. As a proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos built on LaMDA, and demonstrate its fluency and diverse functionality through some illustrative example conversations

    LORSys – Um Sistema de Recomendação de Objetos de Aprendizagem SCORM

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    Objetos de aprendizagem são construídos obedecendo um conjunto de especificações que permitem que eles sejam reusáveis em diferentes contextos. LMSs (Learning Management System) como o MOODLE podem assumir o papel de repositório de objetos de aprendizagem. Porém muitas vezes os alunos, usuários destes LMSs, não têm a experiência necessária para encontrar os objetos de aprendizagem que podem contribuir com o seu aprendizado ou não possuem iniciativa para tal. Este artigo apresenta um sistema de recomendação de objetos de aprendizagem no formato SCORM para o MOODLE, que realiza recomendações utilizando as técnicas de filtragem colaborativa e baseada em conteúdo.
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