CHATBOT FOR KNOWLEDGE – BASED MUSEUM RECOMMENDER SYSTEM (CASE STUDY: MUSEUM IN JAKARTA)

Sistem pemberi rekomendasi yang umum digunakan untuk merekomendasi museum adalah content-based filtering dan collaborative filtering. Tetapi, sistem pemberi rekomendasi tersebut mengalami permasalahan seperti cold start dan data sparsity, karena beberapa museum masih memiliki rating dan feedback yang rendah. Untuk mengatasi masalah tersebut, knowledge-based recommender system dapat digunakan untuk memberikan rekomendasi museum berdasarkan preferensi pengguna, sehingga sistem tidak perlu menggunakan rating dan feedback. Preferensi pengguna bisa didapatkan menggunakan conversational recommender system dengan memanfaatkan percakapan dua arah antara pengguna dengan sistem. Chatbot merupakan salah satu bentuk conversational recommender system yang umum digunakan. Penelitian ini mengembangkan sebuah chatbot untuk merekomendasikan museum di Jakarta menggunakan knowledge-based recommender system. Sistem yang dikembangkan menggunakan Rasa framework untuk membangun chatbot yang mampu melakukan percakapan dengan pengguna. Knowledge graph dan k-nearest neighbor digunakan untuk merekomendasikan museum berdasarkan preferensi pengguna. Berdasarkan evaluasi yang telah dilakukan, sistem yang dikembangkan dapat memahami pesan pengguna dan memberikan rekomendasi museum berdasarkan preferensi pengguna. Tetapi, performa sistem masih dapat dikembangkan supaya sistem dapat diandalkan pada skenario dunia nyata

Contextual Models for Sequential Recommendation

Recommender systems aim to capture the interests of users in order to provide them with tailored recommendations for items or services they might like. User interests are often unique and depend on many unobservable factors including internal moods or external events. This phenomenon creates a broad range of tasks for recommendation systems that are difficult to address altogether. Nevertheless, analyzing the historical activities of users sheds light on the characteristic traits of individual behaviors in order to enable qualified recommendations. In this thesis, we deal with the problem of comprehending the interests of users, searching for pertinent items, and ranking them to recommend the most relevant items to the users given different contexts and situations. We focus on recommendation problems in sequential scenarios, where a series of past events influences the future decisions of users. These events are either the developed preferences of users over a long span of time or highly influenced by the zeitgeist and common trends. We are among the first to model recommendation systems in a sequential fashion via exploiting the short-term interests of users in session-based scenarios. We leverage reinforcement learning techniques to capture underlying short- and long-term user interests in the absence of explicit feedback and develop novel contextual approaches for sequential recommendation systems. These approaches are designed to efficiently learn models for different types of recommendation tasks and are extended to continuous and multi-agent settings. All the proposed methods are empirically studied on large-scale real-world scenarios ranging from e-commerce to sport and demonstrate excellent performance in comparison to baseline approaches

Localizing content: The roles of technical & professional communicators and machine learning in personalized chatbot responses

This study demonstrates that microcontent, a snippet of personalized content that responds to users’ needs, is a form of localization reliant on a content ecology. In contributing to users’ localized experiences, technical communicators should recognize their work as part of an assemblage in which users, content, and metrics augment each other to produce personalized content that can be consumed by and delivered through artificial intelligence (AI)-assisted technology

Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

ATHENA Research Book, Volume 2

ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p