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The original intention of the recommender system is to solve the problem of information explosion, hoping to help users find the content they need more efficiently. In an e-commerce platform, users typically interact with items that they are interested in or need in a variety of ways. For example, buying, browsing details, etc. These interactions are recorded as time-series information. How to use this sequential information to predict user behaviors in the future and give an efficient and effective recommendation is a very important problem. For content providers, such as merchants in e-commerce platforms, more accurate recommendation means higher traffic, CTR (click-through rate), and revenue. Therefore, in the industry, the CTR model for recommendation systems is a research hotspot. However, in the fine ranking stage of the recommendation system, the existing models have some limitations. No researcher has attempted to predict multiple behaviors of one user simultaneously by processing sequential information. We define this problem as the multi-task sequential recommendation problem. In response to this problem, we study the CTR model, sequential recommendation, and multi-task learning. Based on these studies, this paper proposes AMBSN (Attention-based Multi-Behavior Sequential Network). Specifically, we added a transformer layer, the activation unit, and the multi-task tower to the traditional Embedding&MLP (multi-layer perceptron) model. The transformer layer enables our model to efficiently extract sequential behavior information, the activation unit can understand user interests, and the multi-task tower structure makes the model give the prediction of different user behaviors at the same time. We choose user behavior data from Taobao for recommendation published on TianChi as the dataset, and AUC as the evaluation criterion. We compare the performance of AMBSN and some other models on the test set after training. The final results of the experiment show that our model outperforms some existing models. / L’intenzione originale del sistema di raccomandazione è risolvere il problema dell’esplosione delle informazioni, sperando di aiutare gli utenti a trovare il contenuto di cui hanno bisogno in modo più efficiente. In una piattaforma di e-commerce, gli utenti in genere interagiscono con gli articoli a cui sono interessati o di cui hanno bisogno in vari modi. Ad esempio, acquisti, dettagli di navigazione, ecc. Queste interazioni vengono registrate come informazioni di serie temporali. Come utilizzare queste informazioni sequenziali per prevedere i comportamenti degli utenti in futuro e fornire una raccomandazione efficiente ed efficace è un problema molto importante. Per i fornitori di contenuti, come i commercianti nelle piattaforme di e-commerce, una raccomandazione più accurata significa traffico, CTR (percentuale di clic) ed entrate più elevati. Pertanto, nel settore, il modello CTR per i sistemi di raccomandazione è un hotspot di ricerca. Tuttavia, nella fase di classificazione fine del sistema di raccomandazione, i modelli esistenti presentano alcune limitazioni. Nessun ricercatore ha tentato di prevedere più comportamenti di un utente contemporaneamente elaborando informazioni sequenziali. Definiamo questo problema come il problema di raccomandazione sequenziale multi-task. In risposta a questo problema, studiamo il modello CTR, la raccomandazione sequenziale e l’apprendimento multi-task. Sulla base di questi studi, questo documento propone AMBSN (Attention-based Multi-Behavior Sequential Network). In particolare, abbiamo aggiunto uno strato trasformatore, l’unità di attivazione e la torre multi-task al tradizionale modello Embedding&MLP (multi-layer perceptron). Il livello del trasformatore consente al nostro modello di estrarre in modo efficiente le informazioni sul comportamento sequenziale, l’unità di attivazione può comprendere gli interessi degli utenti e la struttura della torre multi-task fa sì che il modello fornisca la previsione di diversi comportamenti degli utenti contemporaneamente. Scegliamo i dati sul comportamento degli utenti da Taobao per la raccomandazione pubblicata su TianChi come set di dati e l’AUC come criterio di valutazione. Confrontiamo le prestazioni di AMBSN e di alcuni altri modelli sul set di test dopo l’allenamento. I risultati finali dell’esperimento mostrano che il nostro modello supera alcuni modelli esistenti.

With growing information availability recommendation systems help users navigate and filter the many options. The home rental market has been pointed out as one of the unexplored areas for recommendations system. This project examines the effects of incorporating historical data for probabilistic weighting and matching algorithms for increased recommendation diversity for a tenant to landlord recommendation system. This was done by implementing two new recommendation systems. The first uses probabilistic weighting to measure the similarity between tenants and landlord homes. The second combines this probabilistic weighting with a variant of the Deferred Acceptance algorithm to enhance recommendation diversity. These two recommendation systems were A/B tested together with the existing tenant recommendation system on the Qasa platform, a digital end-to-end rental apartments marketplace in Sweden. With the objective of having the recommendation system increase landlord engagement a good recommendation was defined as one where the landlord choose to contact the tenant. After the A/B test period, the three recommendation variants were evaluated on Coverage@N, Gini-Index@K, Precision@K and Recall@K. The result revealed that the use of the Deferred Acceptance algorithm did increase the recommendation diversity, but it led to reduced precision in the top recommendations compared to the first new implementation that only used probabilistic weighting. However, the incorporation of historical data for the probabilistic weighting for similarity in booth new recommendation systems showed higher precision and number of contacted tenants compared to the existing tenant recommendation model on the Qasa platform. / Med växande informationstillgänglighet hjälper rekommendationssystem användarna att navigera och filtrera bland många alternativ. Hyresmarknaden har pekats ut som ett av de outforskade områdena för rekommendationssystem. Detta projekt undersöker effekterna av att inkorporera historiska data för statistiska vikter och matchningsalgoritmer för ökad rekommendations mångfald i ett rekommendationssystem från hyresgäster till hyresvärdar. Detta gjordes genom att implementera två nya rekommendationssystem. Det första använder statistiska vikter för att mäta likheten mellan hyresgäster och hyresvärdars bostäder. Det andra kombinerar dessa statistiska vikter med en variant av deferred acceptance algorithm algoritmen för att förbättra rekommendations mångfaldet. Dessa två rekommendationssystem A/B testades tillsammans med det befintliga rekommendationssystemet av hyresgäster på Qasa-plattformen, en digital marknadsplats för andrahandsuthyrning av lägenheter i Sverige. Med målet att rekommendationssystemet skulle öka hyresvärdens engagemang definierades en bra rekommendation som en där hyresvärden valde att kontakta hyresgästen. Efter A/B-testperioden utvärderades de tre rekommendationsvarianterna baserat på Coverage@N, Gini-Index@K, Precision@K och Recall@K. Resultatet visade att användningen av algoritmen för uppskjuten acceptans ökade mångfaldet i ett rekommendationssystem, men det ledde till minskad precision i de första rekommendationerna jämfört med den första nya implementationen som endast använde statistiska vikter. Däremot visade inkorporeringen av historiska data för statistiska vikter vid uträkning av likhet, något som gjordes i båda nya rekommendationssystem, högre precision och fler antal kontaktade hyresgäster jämfört med den befintliga modellen för hyresgästrekommendationer på Qasa-plattformen.

隨著電腦音樂技術的蓬勃發展，合乎情境需求的音樂若被能自動推薦給使用者，將是知識工作者所樂見的。我們提出了一個定義使用者操作情境的情境塑模，定義使用者操作情境，並利用累計專注視窗的轉變，找出使用者的操作情境。同時，我們也提出了音樂推薦塑模，依據使用者的操作情境與聆聽的音樂，分析探勘情境與音樂特徵間的關聯特性，利用探勘出的關聯推薦適合情境的音樂給使用者。在此音樂推薦塑模中，我們採用Content-based Recommendation的作法。我們分析音樂的特徵值，並發展MAML(Multi-attribute Multi-label)的分類演算法以及Probability Measure二種方法來探勘情境屬性與音樂特徵間的關聯特性。根據探勘出的關聯特性，找出適合情境的音樂特徵，再從音樂資料庫中推薦符合音樂特徵的音樂給使用者。本論文的符合使用者操作情境的音樂推薦系統是利用Windows Hook API實作。經實驗證明，本論文方法在符合情境的音樂推薦上，擁有近七成準確率。 / With the development of digital music technology, knowledge workers will be delighted if the music recommendation system is able to automatically recommend music based on the operating context in the desktop. The context model and context identification algorithm are proposed to define the operating context of users and to detect the transition of context based on the changes of focused windows. Two association discovery mechanisms, MMAL (Multi-attribute Multi-label) algorithm and PM (Probability Measure), are proposed to discover the relationships between context features and music features. Based on the discovered rules, the proposed music recommendation mechanism recommends music to the user from the music database according to the operating context of users. The context-based recommendation system is implemented using Windows Hook API. Experimental results show that near 70% accuracy can be achieved.

在WEB2.0的時代，網際網路中充斥著各式各樣的互動式平台。就音樂網站而言，使用者除了聽音樂外，更開始習慣於虛擬空間中交流及分享意見，並且在這些交流、分享的過程中留下他們的足跡，間接的提供許多帶有個人色彩的資訊。利用這些資訊，更貼近使用者的推薦系統因應而生。本研究中，將針對使用者過去存取過的音樂特徵以及使用者於系統中留下的文字評論特徵這兩個部份的資料，做音樂特徵的擷取、找尋具有價值的音樂特徵區間、建立使用者音樂特徵偏好，以及文字特徵的擷取、建立使用者文字特徵偏好。接著，採用協同式推薦方式，將具有相同興趣的使用者分於同一群，推薦給使用者與之同群的使用者的喜好物件，但這些推薦之物件為該使用者過去並沒有任何記錄於這些喜好物件上之物件。我們希望對於音樂推薦考慮的開始不只是音樂上之特徵，更包含了使用者交流、互動中留下的訊息。 / In the era of Web2.0, it is flooded with a variety of interactive platforms on the internet. In terms of music web site, in addition to listening to music, users got used to exchanging their comments and sharing their experiences through virtual platforms. And through the process of exchanging and sharing, they left their footprints. These footprints indirectly provide more information about users that contains personal characteristics. Moreover, from this information, we can construct a music recommendation system, which provides personalized service. In this research, we will focus on user’s access histories and comments of users to recommend music. Moreover, the user’s access histories are analyzed to derive the music features, then to find the valuable range of music features, and construct music profiles of user interests. On the other hand, the comments of users are analyzed to derive the textual features, then to calculate the importance of textual features, and finally to construct textual profiles of user interests. The music profile and the textual profile are behaviors for user grouping. The collaborative recommendation methods are proposed based on the favorite degrees of the users to the user groups they belong to.

By conducting sound research to understand the concepts surrounding rioting and efficient riot response tactics, professionals, especially whose main job is to ensure the tranquility in the society, will be better prepared to deal with all kinds of civil movements. The purpose of this study, consequently, is to meet the growing need for educational materials in this area and to provide riot response case studies, which demonstrate the numerous administrative challenges faced by law enforcement decision makers. In this study, seven major riots from throughout the United States are discussed including the Hay Market Riot of 1886, the Watts Riot of 1965, and Los Angeles Riots of 1992. Each riot case is studied in five different and independent stages: the setting and pre-disturbance situation, basic causes of the event, the disturbance situation, the response to the riot, and the aftermath of the incident. The study of all of these stages is intended to help police administrators acquire a general perspective on collective violence, and help them prevent future occurrences in their jurisdictions. In this thesis a special reference is given to the deficiencies of American riot policing and some recommendations were formed accordingly. Therefore, the study concludes with a list of general recommendations, which are crucially important for concerned officials to pay attention before, during, and after a riot.

Bakgrund: Kommunikation är en avancerad process som innefattar utbyte av information och kan ske genom tal, skrift och tecken. En stor del av sjuksköterskans arbete består av att förmedla information, ofta i form av överrapporteringar. Brister i kommunikationen leder lätt till missförstånd och att delar av informationen faller bort, vilket innebär en stor risk för patientsäkerheten. Kommunikationsverktyg är ett samlingsbegrepp för olika mallar som är framtagna för att underlätta och strukturera upp kommunikation. Ett sådant verktyg är SBAR. Syfte: Syftet med föreliggande litteraturstudie var att beskriva följderna av sjuksköterskors användning av SBAR vid informationsöverföring inom sjukvården, samt att beskriva de valda artiklarnas tillvägagångssätt för datainsamling. Metod: Litteraturstudien hade en deskriptiv design och baserades på 11 sökta artiklar. Databaserna Cinahl och Medline via PubMed användes för artikelsökning. Huvudresultat: Resultatet visade att SBAR förbättrade kommunikationen och gjorde den mer lättförståelig och effektiv. Sjuksköterskor blev tryggare i sin yrkesroll och uppfattades vara kunnigare, mer fokuserade och bättre förberedda. Det blev en större medvetenhet bland sjuksköterskor gällande patientsäkerheten och färre incidenter rapporterades. Slutsats: SBAR tycks vara ett lämpligt och effektivt kommunikationsverktyg att använda inom sjukvården, sett både ur ett patient- och personalperspektiv, samt att vårdinstanser bör införa och utbilda sin personal i att använda SBAR. / Background: Communication is an advanced process which includes an exchange of information and can be performed through speech, writing or signs. A big part of the nursing profession is to transfer information, commonly in forms of handovers. Errors in communication can easily lead to misunderstandings and that parts of the information is lost, which can be a grave risk for the patient security. Communication tools are a generic term for templates made to help and structure communication. One of those templates is SBAR. Purpose: The purpose of the following literature study was to describe the progressions of that nurses use SBAR at information transfers within the healthcare industry, and to describe the chosen articles method for gathering data. Method: The literature study had a descriptive design and was based on 11 found articles. The databases Cinahl and Medline through PubMed were used for the search for articles. Main result: The result showed that SBAR improved the communication and made it easier to understand and more efficient. Nurses became more secure in their working role and were perceived as to have more knowledge, more focused and better prepared.There was an improved consciousness among nurses concerning patient security and fewer incidents were reported. Conclusion: SBAR seems to be a suitable and efficient communication tool to be used within the healthcare industry, from both a patient perspective and a staff perspective, and that healthcare agencies should introduce and train their staff in using SBAR.

Bibliographical identification Title of Bachelor work: The size and differences in physical activity in the weekly mode in adolescent soccer players on performance level Place of work: UK FTVS Author: Bc. Michal Hojdar Field of study: Physical education and sport Head of work: Mgr. Jakub Kokštejn, Ph.D. Defence year: 2015 Generalization: This thesis is focused on the analysis of physical activity weekly regimen of adolescent soccer players at performance level who practice regular physical activity. The primary objective is to determine the differences in the size of physical activities in the category of younger and older adolescents, and then compare the results obtained with each other and with health recommendations for these ages. Younger adolescents consist of U16 and U17 players in age from 15 to 17 years. Older adolescents include soccer players U19 aged between 17 and 19 years. Research is aimed to the players of football club FC MAS Táborsko. To determine the size and composition of physical activity was used accelerometer ActiGraph GT3X. Obtained data are processed in the program ActiLife Lifestyle Monitor Software. To ensure that the measured values are relevant and comparable, were used statistical methods of calculations, such as arithmetic mean and standard deviation. For clarity and ease of...

Title of Bachelor thesis: Physical activity for health in sport youth Author: Bc. Jan Kokštejn Head of work: Mgr. Jakub Kokštejn, Ph.D. Aim: Findings the size of physical activity in youth soccer players during a week mode and comparison of selected indicators with recommendations of physical aktivity for health promotion. To identify possible differences in physical activity between younger (16-17 years old) and older (18-19 years old) players in adolescent category. Methods: The research samples consist of younger soccer players (n=25; 16,2±0,9 years old) and older soccer players (n=25; 18,6±0,3 years old). Accelerometers Actigraph GT3X were used for determining level of physical activity during the week. Results: The average daily amount of modern to high intensity physical activity exceeded in both groups of soccer players worldwide used recommendations, which is 60 minutes. For daily number of steps, the group of younger players exceeded recommended amount according to Sigmund et al. (2005). The group of older players remained just below this threshold, but within the recommended range. In terms of energy output both groups reached values between 8-9 kcal.kg-1 .day-1 , according to selected criteria met physical activity recommendations. Energy output value and the number of steps did not...

Dans cette thèse, nous proposons des algorithmes de recommandation sémantique et sociale, qui recommandent un produit pour les utilisateurs qui sont connectés par un réseau de collaboration sociale. Ces algorithmes utilisent deux types d'informations : information sémantique et information sociale .L' information sémantique est basée sur la pertinence sémantique entre les utilisateurs et le produit à recommandé, tandis que l' information sociale est basée sur la position de l'utilisateur et de leur type et de la qualité des connexions entre eux dans le réseau de collaboration . Enfin, nous utilisons l'algorithme de parcoure profondeur (DFS) et l'algorithme de parcoure en largeur (BFS), pour explorer le réseau social.Utilisation de l' information sémantique et l'information sociale , dans le système de recommandation , nous aide à explorer partiellement le réseau social , ce qui nous conduit à réduire la taille des données explorées et de minimiser le temps de recherche dans le réseau.Nous appliquons nos algorithmes sur des données réelles : MovieLens et Amazon , et nous comparons la précision de la performance de nos algorithmes avec les algorithmes de recommandation classiques , comme l'algorithme de filtrage collaborative et l'algorithme hybrideNos résultats montrent un taux de précision satisfaisants , et une performance très significative du temps d'exécution et de la taille des données explorées , par rapport aux autres algorithmes de recommandation classiques .En fait , l'importance de nos algorithmes repose sur le fait que ces algorithmes explorent une très petite partie du graphe , au lieu d'explorer tout le graphe que les méthodes de recherche classiques , et encore donnent une bonne précision par rapport aux autres algorithmes de recommandation classiques . Donc , en minimisant la taille des données recherchées n'influence pas mal la précision des résultats . / In this thesis we propose semantic-social recommendation algorithms, that recommend an input item to users connected by a collaboration social network. These algorithms use two types of information: semantic information and social information.The semantic information is based on the semantic relevancy between users and the input item; while the social information is based on the users position and their type and quality of connections in the collaboration social network. Finally, we use depth-first search and breath-first search strategies to explore the graph.Using the semantic information and the social information, in the recommender system, helps us to partially explore the social network, which leads us to reduce the size of the explored data and to minimize the graph searching time.We apply our algorithms on real datasets: MovieLens and Amazon, and we compare the accuracy an the performance of our algorithms with the classical recommendation algorithms, mainly item-based collaborative filtering and hybrid recommendation.Our results show a satisfying accuracy values, and a very significant performance in execution time and in the size of explored data, compared to the classical recommendation algorithms.In fact, the importance of our algorithms relies on the fact that these algorithms explore a very small part of the graph, instead of exploring all the graph as the classical searching methods, and still give a good accuracy compared to the other classical recommendation algorithms. So, minimizing the size of searched data does not badly influence the accuracy of the results.