A food recipe recommendation system based on nutritional factors in the Finnish food communit

Abstract. This thesis presents a comprehensive study on the relationships between user feedback, recipe content, and additional factors in the context of a recipe recommendation system. The aim was to investigate the influence of various factors on user ratings and comments related to nutritional variables, while also exploring the potential for personalized recipe suggestions. Statistical analysis, clustering techniques, and sentiment analysis were employed to analyze a dataset of food recipes and user feedback. We determined that user feedback is a complex phenomenon influenced by subjective factors beyond recipe content alone. Cluster analysis identified four distinct clusters within the dataset, highlighting variations in nutritional values and sentiment among recipes. However, due to an imbalanced distribution within the clusters, these relationships were not considered in the recommendation system. To address the absence of user-related data, a content-based filtering approach was implemented, utilizing nutritional factors and a health factor calculation. The system provides personalized recipe recommendations based on nutritional similarity and health considerations. A maximum limit of 20 recommended recipes was set, allowing users to specify the desired number of recommendations. The accompanying API also provides a mean squared error metric to assess recommendation quality. This research contributes to a better understanding of user preferences, recipe content, and the challenges in developing effective recommendation systems for food recipes

\u27Friends Group\u27 in Recommender Systems: Effects of User Involvement in the Formation of Recommending Groups

How can we improve the acceptance of recommendations in collaborative systems? The group identity of recommenders and recipient involvement in group formation impacts on the likelihood that users of social collaborative systems would accept recommendations provided on it. We introduce the term \u27friends group\u27 to describe a sub-group of the \u27neighbors group\u27 in recommender systems that is not solely rank-dependent, as opposed to \u27neighbors\u27 that are assigned by rating similarity. The \u27friends group\u27 is unique because of the user\u27s involvement in its formation and the user\u27s ability to choose the characteristics of its members. The latter aspect corresponds to Festinger\u27s Social Comparison Theory , suggesting that \u27neighbors\u27 (like-minded groups) are relevant for \u27low-risk\u27 domains whereas similarity-based \u27friends\u27 are more relevant for \u27high-risk\u27 domains. We conducted a two year field study, using QSIA, a Web-based Java-programmed collaborative system for collection, management, sharing and assignment of learning knowledge items. QSIA was implemented in over ten courses in several universities. QSIA database and logs contained approximately 31,000 records of items-seeking acts, 3,000 users, 10,000 items, 3,000 rankings and knowledge items from 30 domains. We found that the difference between acceptance and rejection ratios of recommendations when the items originated from an advising group comprised of \u27friends\u27, is significantly higher than when the advising group is the more commonly known \u27neighbors group\u27. The difference increases for frequently recommended as opposed to other items and for experienced as opposed to \u27average\u27 users. Our longitudinal analysis indicates a positive learning curve for experienced users, who, over time, increasingly preferred \u27friends group\u27 over \u27neighbors group\u27 as their experience with the system increases. Also, users chose their own group to participate in the advising group significantly more than other groups. The contribution of this study is in explicating the relationship between the perceived quality of the recommendation (measured in terms of usage actions ), and the user\u27s involvement in the formation of the advising group. The major implication of our findings for the development of recommender systems is the need to enhance involvement of recommendation seekers in the process of forming the advising group. Developers of recommender systems should consider increasing users\u27 control over relevant characteristics of the members of this group

Personalized News Recommender using Twitter

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily swamped by information of little interest to them. News recommender systems are one approach to help users find interesting articles to read. News recommender systems present the articles to individual users based on their interests rather than presenting articles in order of their occurrence. In this thesis, we present our research on developing personalized news recommendation system with the help of a popular micro-blogging service Twitter . The news articles are ranked based on the popularity of the article that is identified with the help of the tweets from the Twitter\u27s public timeline. Also, user profiles are built based on the user\u27s interests and the news articles are ranked by matching the characteristics of the user profile. With the help of these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity and their relevance to the user profile

Music feature extraction and analysis through Python

En l'era digital, plataformes com Spotify s'han convertit en els principals canals de consum de música, ampliant les possibilitats per analitzar i entendre la música a través de les dades. Aquest projecte es centra en un examen exhaustiu d'un conjunt de dades obtingut de Spotify, utilitzant Python com a eina per a l'extracció i anàlisi de dades. L'objectiu principal es centra en la creació d'aquest conjunt de dades, emfatitzant una àmplia varietat de cançons de diversos subgèneres. La intenció és representar tant el panorama musical més tendenciós i popular com els nínxols, alineant-se amb el concepte de distribució de Cua Llarga, terme popularitzat com a "Long Tail" en anglès, que destaca el potencial de mercat de productes de nínxols amb menor popularitat. A través de l'anàlisi, es posen de manifest patrons en l'evolució de les característiques musicals al llarg de les dècades passades. Canvis en característiques com l'energia, el volum, la capacitat de ball, el positivisme que desprèn una cançó i la seva correlació amb la popularitat sorgeixen del conjunt de dades. Paral·lelament a aquesta anàlisi, es concep un sistema de recomanació musical basat en el contingut del conjunt de dades creat. L'objectiu és connectar cançons, especialment les menys conegudes, amb possibles oients. Aquest projecte ofereix perspectives beneficioses per a entusiastes de la música, científics de dades i professionals de la indústria. Les metodologies implementades i l'anàlisi realitzat presenten un punt de convergència de la ciència de dades i la indústria de la música en el context digital actualEn la era digital, plataformas como Spotify se han convertido en los principales canales de consumo de música, ampliando las posibilidades para analizar y entender la música a través de los datos. Este proyecto se centra en un examen exhaustivo de un conjunto de datos obtenido de Spotify, utilizando Python como herramienta para la extracción y análisis de datos. El objetivo principal se centra en la creación de este conjunto de datos, enfatizando una amplia variedad de canciones de diversos subgéneros. La intención es representar tanto el panorama musical más tendencioso y popular como los nichos, alineándose con el concepto de distribución de Cola Larga, término popularizado como Long Tail en inglés, que destaca el potencial de mercado de productos de nichos con menor popularidad. A través del análisis, se evidencian patrones en la evolución de las características musicales a lo largo de las décadas pasadas. Cambios en características como la energía, el volumen, la capacidad de baile, el positivismo que desprende una canción y su correlación con la popularidad surgen del conjunto de datos. Paralelamente a este análisis, se concibe un sistema de recomendación musical basado en el contenido del conjunto de datos creado. El objetivo es conectar canciones, especialmente las menos conocidas, con posibles oyentes. Este proyecto ofrece perspectivas beneficiosas para entusiastas de la música, científicos de datos y profesionales de la industria. Las metodologías implementadas y el análisis realizado presentan un punto de convergencia de la ciencia de datos y la industria de la música en el contexto digital actualIn the digital era, platforms like Spotify have become the primary channels of music consumption, broadening the possibilities for analyzing and understanding music through data. This project focuses on a comprehensive examination of a dataset sourced from Spotify, with Python as the tool for data extraction and analysis. The primary objective centers around the creation of this dataset, emphasizing a diverse range of songs from various subgenres. The intention is to represent both mainstream and niche musical landscapes, aligning with the Long Tail distribution concept, which highlights the market potential of less popular niche products. Through analysis, patterns in the evolution of musical features over past decades become evident. Shifts in features such as energy, loudness, danceability, and valence and their correlation with popularity emerge from the dataset. Parallel to this analysis is the conceptualization of a music recommendation system based on the content of the data set. The aim is to connect tracks, especially lesser-known ones, with potential listeners. This project provides insights beneficial for music enthusiasts, data scientists, and industry professionals. The methodologies and analyses present a convergence of data science and the music industry in today's digital contex

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented