The Case for Graph-Based Recommendations

Recommender systems have been intensively used to create personalised profiles, which enhance the user experience. In certain areas, such as e-learning, this approach is short-sighted, since each student masters each concept through different means. The progress from one concept to the next, or from one lesson to another, does not necessarily follow a fixed pattern. Given these settings, we can no longer use simple structures (vectors, strings, etc.) to represent each user's interactions with the system, because the sequence of events and their mapping to user's intentions, build up into more complex synergies. As a consequence, we propose a graph-based interpretation of the problem and identify the challenges behind (a) using graphs to model the users' journeys and hence as the input to the recommender system, and (b) producing recommendations in the form of graphs of actions to be taken

Recommending Structured Objects: Paths and Sets

Recommender systems have been widely adopted in industry to help people find the most appropriate items to purchase or consume from the increasingly large collection of available resources (e.g., books, songs and movies). Conventional recommendation techniques follow the approach of ``ranking all possible options and pick the top'', which can work effectively for single item recommendation but fall short when the item in question has internal structures. For example, a travel trajectory with a sequence of points-of-interest or a music playlist with a set of songs. Such structured objects pose critical challenges to recommender systems due to the intractability of ranking all possible candidates. This thesis study the problem of recommending structured objects, in particular, the recommendation of path (a sequence of unique elements) and set (a collection of distinct elements). We study the problem of recommending travel trajectories in a city, which is a typical instance of path recommendation. We propose methods that combine learning to rank and route planning techniques for efficient trajectory recommendation. Another contribution of this thesis is to develop the structured recommendation approach for path recommendation by substantially modifying the loss function, the learning and inference procedures of structured support vector machines. A novel application of path decoding techniques helps us achieve efficient learning and recommendation. Additionally, we investigate the problem of recommending a set of songs to form a playlist as an example of the set recommendation problem. We propose to jointly learn user representations by employing the multi-task learning paradigm, and a key result of equivalence between bipartite ranking and binary classification enables efficient learning of our set recommendation method. Extensive evaluations on real world datasets demonstrate the effectiveness of our proposed approaches for path and set recommendation

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 816303 and from the Plan Propio de Investigación y Transferencia of the University of Granada under the program “Intensificación de la Investigación, modalidad B”.Acknowledgments: This work is part of the doctoral thesis of Daniel Hinojosa-Nogueira conducted within the context of the “Program of Nutrition and Food Sciences” at the University of Granada and part of the doctoral thesis of Bartolome Ortiz-Viso conducted within the context of the “Program of Information and Communication technologies” at the University of Granada.Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user’s needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.European Union’s Horizon 2020 research and innovation programme under grant agreement No 816303Plan Propio de Investigación y Transferencia of the University of Granada under the program “Intensificación de la Investigación, modalidad B

Social-economics Analysis and Community Empowerment the Watershed of Kedaung at Gajah Mungkur Reservoir Wonogiri-cental Java

Gajah Mungkur Reservoir is designed to storage reservoir for 100 years since the operation in 1982-2082. Currently the sedimentation rate reaches 8 millimeters per year, while the assumption of sediment rate (mud) is only 2 millimeters per year. This research was conducted to save reservoir and river basin environment in Gajah Mungkur Reservoir. This study aims to determine the socio-economic conditions and behavior of people in the Keduang River watershed area. This research also formulates community empowerment model in Keduang River area and recommends grand design management system involving various related parties

Development and validation of a clinical and computerised Decision Support System for Management of Hypertension (DSS-HTN) at a Primary Health Care (PHC) setting

Background: Hypertension remains the top global cause of disease burden. Decision support systems (DSS) could provide an adequate and cost-effective m

Factors Affecting E-Scooter Sharing Purchase Intention: An Analysis Using Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

Transportation uses a significant amount of energy and burns most of the world energy consumers. As a result, it gives effect to the environment, such as air pollution in the forms of carbon dioxide, carbon monoxide, nitrogen oxide, hydrocarbons or volatile organic compounds, and particulate matter. Those compounds contribute a phenomenon called global warming. Within the transportation sector, road transport is the largest contributor to global warming. To cope with global warming, environmental regulations in developed countries are trying to reduce the individual vehicle's emissions. However, this has been counterbalanced by an increase in the number of vehicles and increased use of each vehicle. Therefore, micro-mobility may alleviate several challenges facing big cities today and offer more sustainable urban transportation. This research utilizes the framework of the UTAUT2 to identify and build a quantitative approach to identify factors related to the purchase intention factors of e-scooter sharing. The 200 respondents' field data were collected in Jakarta Metropolitan Area (Jabodetabek) as a rapid increase in pollution level. The linear regression study revealed that the consumers' purchase intention of e-scooter sharing is shaped by seven main factors: performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, price value, and habit. Those factors can explain 81 percent of the field data. Moreover, a brief recommendation for related stakeholders based on the research result is proposed to increase the adoption of e-scooter sharing. The practical implication resulted from this analysis are suggested policy measures the e-scooter sharing environmentally impact potency and strengthening circular economy as a part of green economy achievement in the communities