Automatic Personalized Playlist Generation

Käesolevas magistritöös on esitatud automaatse personaliseeritud pleilisti tekitaja probleemi lähenemisviiside uuring. Lisaks teoreetilise tausta lühiülevaatele me dokumenteerisime oma lähenemist: meie poolt tehtud katsed ning nende tulemused. Meie algoritm koosneb kahest põhiosast: pleilisti hindamisfunktsiooni konstrueerimine ning pleilisti genereerimisstrateegia valik. Esimese ülesande lahendamiseks on valitud Naive Bayes klassifitseerija ning 5-elemendiline MIRtoolbox tööristakasti poolt kavandatud audio sisupõhiste attribuutide vektor, mis klassiitseerivad pleilisti heaks või halvaks 82% täpsusega - palju parem kui juhuslik klassifitseerija (50%). Teise probleemi lahendamiseks proovisime kolm genereerimisalgoritmi: lohistus (Shuffle), randomiseeritud otsing (Randomized Search) ning geneetiline algoritm (Genetic Algorithm). Vastavalt katsete tulemustele kõige paremini ja kiiremini töötab randomiseeritud otsingu algoritm. Kõik katsed on tehtud 5 ning 10 elemendilistel pleilistidel. Kokkuvõttes, oleme arendanud automatiseeritud personaliseeritud pleilisti tekitaja algoritmi, mis vastavalt meie hinnangutele vastab ka kasutaja ootustele rohkem, kui juhuslikud lohistajad. Algoritmi võib kasutada keerulisema pleilistide konstrueerimiseks

Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction

Maximizing satisfaction from offering features as part of the upcoming release(s) is different from minimizing dissatisfaction gained from not offering features. This asymmetric behavior has never been utilized for product release planning. We study Asymmetric Release Planning (ARP) by accommodating asymmetric feature evaluation. We formulated and solved ARP as a bi-criteria optimization problem. In its essence, it is the search for optimized trade-offs between maximum stakeholder satisfaction and minimum dissatisfaction. Different techniques including a continuous variant of Kano analysis are available to predict the impact on satisfaction and dissatisfaction with a product release from offering or not offering a feature. As a proof of concept, we validated the proposed solution approach called Satisfaction-Dissatisfaction Optimizer (SDO) via a real-world case study project. From running three replications with varying effort capacities, we demonstrate that SDO generates optimized trade-off solutions being (i) of a different value profile and different structure, (ii) superior to the application of random search and heuristics in terms of quality and completeness, and (iii) superior to the usage of manually generated solutions generated from managers of the case study company. A survey with 20 stakeholders evaluated the applicability and usefulness of the generated results

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Investigating the usability of software systems for music production and distribution

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe work here aims to evaluate the usability of software applications and define their quality for stakeholders in the music industry. Initial work focuses on standardised tools and procedures and sets benchmarks for performance times and completion rates across software packages, before aiming to make some suggestions about how improvements could be made in the design of said interfaces. Further work goes on to explore industry tools in the context of the real world, live performance tools, categorising them according to purpose and evaluating their success. Finally, a series of workshops and discussion groups aim to identify problems and solutions, suggesting a novel way of evaluating music information systems from a usability perspective. The work here explores usability issues in terms of efficiency, effectiveness and user satisfaction, showing that systems can fail in all three categories. While typical software tools such as Cubase are found to be somewhat usable, the changing requirements of users mean that software systems are no longer effective in performing day to day tasks required of them. There is further exploration into how software tools are used incorrectly or inefficiently, where learning curves are too steep to overcome and where systems inevitably fail. The thesis culminates in a suggested set of heuristics which can be used to evaluate current systems and used as a guideline in developing human-centred systems within the context of music performance and production. The work highlights the strengths of existing systems in terms of enabling creativity and providing an efficient platform for content creation, while making suggestions about future directions of such systems including a discussion in social web integration and pervasive interfaces

Integration of a recommender system into an online video streaming platform

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system.Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation