scholarly journals Preference Oriented Mining Techniques for Location based Point Search

2021 ◽  
Author(s):  
S M Nazmuz Sakib
Keyword(s):  
User Preferences ◽  
Shopping Malls ◽  
Score System ◽  
Algorithm Efficiency ◽  
Feedback Learning ◽  
Public Evaluation ◽  
Collaborative Filtering Algorithm ◽  
Point Search ◽  
Nearest Points ◽  
User Location

With the development of internet and wireless technologies, location based search is among the most discussed topic in current era. To address issues of location based search a lot of research has been done but it mainly focused on the specific aspects of the domain like most of the studies focused, on the search of nearby restaurants, shopping malls, hospitals, stores etc., by utilizing location of users as searching criteria. Problem with these studies is that users might not be satisfied by their results and the sole reason behind this might be the absence of user preferences in the search criteria. There exists some studies which focused user preferences along with user location and query time and proposed some frameworks but they are only limited to stores and their research cannot be scaled to other points like schools, hospitals, doctors , petrol pumps, gas station etc. Moreover there exist scalability issues in their recommended algorithms along with some data credibility issues in their public evaluations strategies. Our proposed research is going to present a novel location based searching technique not only for stores but for any point. The presented solution has overcome issues faced in previous research studies and possesses capability to search for “K” nearest points which are most preferable by user, by utilizing searching time as well as query location. Our research has proposed two feedback learning algorithms and one ranking algorithm. To increase the credibility of public evaluation score, system have utilized Google ranking approach while calculating the score of the point. To make user recommendations nonvolatile along with improving recommendations algorithm efficiency, proposed system have introduced item to item collaborative filtering algorithm. Through experimental evaluations on real dataset of yelp.com presented research have shown significant gain in performance and accuracy.

scholarly journals Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenning Yuan ◽  
Jong Han Lee ◽  
Sai Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Preferences ◽  
Classification Model ◽  
Filtering Algorithm ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Low Dimensional ◽  
Preference Matrix

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

scholarly journals An Ubiquitous and Non Intrusive System for Pervasive Advertising using NFC and Geolocation Technologies and Air Hand Gestures

2014 ◽  
Vol 10 (4) ◽  
pp. 361-384
Author(s):  
Francisco M. Borrego-Jaraba ◽  
Irene Luque Ruiz ◽  
Miguel Ángel Gómez-Nieto
Keyword(s):  
Mobile Phones ◽  
Communication Technology ◽  
Instant Messaging ◽  
Near Field ◽  
User Preferences ◽  
Near Field Communication ◽  
Hand Gestures ◽  
Large Displays ◽  
Advertising Strategies ◽  
User Location

In this paper we present a pervasive proposal for advertising using mobile phones, Near Field Communication, geolocation and air hand gestures. Advertising post built by users in public/private spaces can store multiple ads containing any kind of textual, graphic or multimedia information. Ads are automatically shows in the mobile phone of the users using a notification based process considering relative user location between the posts and the user preferences. Moreover, ads can be stored and retrieved from the post using hand gestures and Near Field Communication technology. Secure management of information about users, posts, and notifications and the use of instant messaging enable the development of systems to extend the current advertising strategies based on Web, large displays or digital signage.

A mobile service using anonymous location-based data: finding reading rooms

2015 ◽  
Vol 32 (1) ◽  
pp. 32-44 ◽  
Author(s):  
Shang Gao ◽  
John Krogstie ◽  
Trond Thingstad ◽  
Hoang Tran
Keyword(s):  
Technology Acceptance ◽  
Design Methodology ◽  
Design Science ◽  
Mobile Service ◽  
Acceptance Test ◽  
University Campus ◽  
Shopping Malls ◽  
Content Type ◽  
Usability Test ◽  
User Location

Purpose – The purpose of this paper is to develop a mobile service, based on anonymous location-based data, to help students find available reading rooms on a university campus. To evaluate this mobile service, both a usability test and a technology acceptance test were carried out. Design/methodology/approach – The research followed a design science approach, including developing a prototype and evaluating the developed prototype. Findings – The results from the usability test indicated good usability of the developed mobile service. The results from the technology acceptance test demonstrated students’ intention to use this mobile service. Most respondents indicated that they would like to use this mobile service to find available reading rooms when they are on campus. Research limitations/implications – The results imply that there are other contexts where anonymous location-based data are also useful. A similar mobile service can be developed for other contexts, such as, hospital complexes, shopping malls, and airports. Originality/value – To the authors best knowledge, the authors have not found any mobile services aiming at counting the density of people residing in a room by using anonymous user location-based data on a university campus. This research fills this gap by developing the mobile service, called finding reading rooms.

Fuzzy trust based collaborative filtering analysis for mobile user preferences

2021 ◽  
pp. 1-7
Author(s):  
Chengfang Tan ◽  
Lin Cui ◽  
Xiaoyin Wu
Keyword(s):  
Collaborative Filtering ◽  
Rapid Development ◽  
Mobile User ◽  
User Preferences ◽  
Data Matrix ◽  
User Interest ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
The Impact ◽  
Fuzzy Trust

With the rapid development of mobile terminal devices, mobile user activities can be carried out anytime and anywhere through various mobile terminals. The current research on mobile communication network is mainly focused on extracting useful and interesting information for mobile user from massive and disordered information. However, the sparsity of scoring data matrix results in low quality of recommendation algorithm. In order to overcome this drawback, the traditional collaborative filtering algorithm is improved. First, the user-interest matrix and item-feature matrix were obtained by analyzing mobile user behavior and item attributes. Fuzzy trust based model is utilized for collaborative filtering analysis for mobile user preferences. Then, the similarity between different mobile users was calculated by weighted calculation. With this method, mobile user preference can be predicted effectively, making it possible to recommend rational resource and waste less time in extracting resources out of the massive information. Experimental results show that the proposed algorithm reduces the mean absolute error (MAE) and the impact of sparse scoring matrix data compared with the traditional collaborative filtering algorithm, and improves the recommendation effect to a certain extent.

On Personalizing Web Services Using Context

2011 ◽  
pp. 232-253 ◽  
Author(s):  
Zakaria Maamar ◽  
Soraya Kouadri Mostéfaoui ◽  
Qusay H. Mahmoud
Keyword(s):  
Web Services ◽  
Web Service ◽  
User Preferences ◽  
Temporal Perspective ◽  
User Context ◽  
Different Types ◽  
User Location ◽  
The Web

This chapter presents a context-based approach for Web services personalization so that user preferences are accommodated. Preferences are of different types, varying from when the execution of a Web service should start to where the outcome of this execution should be delivered according to user location. Besides user preferences, it will be discussed in this chapter that the computing resources on which the Web services operate have an impact on their personalization. Indeed, resources schedule the execution requests that originate from multiple Web services. To track the personalization of a Web service from a temporal perspective (i.e., what did happen, what is happening, and what will happen), three types of contexts are devised and referred to as user context, Web service context, and resource context.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory

2021 ◽  
Vol 11 (2) ◽  
pp. 843
Author(s):  
Nihong Yang ◽  
Lei Chen ◽  
Yuyu Yuan
Keyword(s):  
Collaborative Filtering ◽  
Proactive Inhibition ◽  
User Preferences ◽  
Retroactive Inhibition ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Memory Inhibition ◽  
Real World Datasets ◽  
Sequential Models ◽  
The Relationship

Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user’s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user’s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU.

scholarly journals Tourism Recommender System Based on Cognitive Similarity Between Cross-Cultural Users

2021 ◽  
Author(s):  
Luong Vuong Nguyen ◽  
Tri-Hai Nguyen ◽  
Jason J. Jung
Keyword(s):  
Recommendation System ◽  
Classical Method ◽  
Pearson Correlation ◽  
User Preferences ◽  
Cross Cultural ◽  
Shopping Malls ◽  
Cultural Effects ◽  
Initial Dataset ◽  
Extensive Information ◽  
Tourism Services

Nowadays, the speedy increasing information in tourism services since a massive amount of data is constructed by tourists experiences. The recommendation systems are widely applied to tourism services and focus on determining personalized user preferences to handle this extensive information. Exploiting the different cultural effects rarely consider in recent studies despite this factor influences recommendation based on user preferences. Furthermore, existing research only evaluates the relevance of cultural differences to their recommendation, rather than using the cross-cultural factors to recommendations systems. This paper proposes the collaborative filtering recommendation system based on similar tourist places where users from different cross-cultural can share their spatial experiences. To do that, we first collect user feedback about similar tourist places from many nationalities (consider as the cultures). We then exploit this feedback to define similar cross-cultural users (neighbors) based on a cognitive similarity. Finally, the system generates personalized recommendations based on user experiences and their neighbors. The initial dataset collected from TripAdvisor, consisting of four types such as hotels, restaurants, shopping malls, and attractions, is provided to the feedback collection function in our experiment. We were using the classical method, user-based Pearson correlation, as a baseline to demonstrate the performance of our proposed method. The result shows that the proposed system outperforms the baseline in terms of MAE and RMSE metrics.

(Impending) Pathological Fracture

Swiss Surgery ◽  
2002 ◽  
Vol 8 (2) ◽  
pp. 81-87 ◽  
Author(s):  
Sutter ◽  
Regazzoni
Keyword(s):  
Pathological Fracture ◽  
Chirurgische Behandlung ◽  
Pathologische Fraktur ◽  
Score System ◽  
Postoperative Radiotherapie ◽  
Pathologische Frakturen

Pathologische Frakturen werden, bedingt durch die Zunahme der Inzidenz von Karzinomen und die längeren überlebenszeiten, in Zukunft häufiger behandelt werden müssen. Das Skelett ist das dritthäufigste Ziel-Organ von Metastasen. Lungentumor-Metastasen scheinen zuzunehmen, das Mammakarzinom bleibt aber der häufigste Primärtumor. Am häufigsten sind Metastasen im Bereiche der Wirbelsäule lokalisiert, Frakturen treten jedoch meistens am Femur auf. Eine pathologische Fraktur sowie fast immer auch eine "drohende pathologische Fraktur" stellen eine absolute Operationsindikation dar. Eine genaue Definition der "drohenden Fraktur" fehlt zwar, doch ist heute allgemein akzeptiert, dass mindestens 50% der Knochenmasse zerstört sein müssen, damit die Metastase im konventionellen Röntgenbild sichtbar wird und somit von einer drohenden Fraktur gesprochen werden kann. Als Hilfe zur Abschätzung des Frakturrisikos hat sich das Score System nach Mirels bewährt. Anhand von 4 Parametern (Lokalisation, Grösse, Typ, Schmerzen) kann das Frakturrisiko abgeschätzt werden. Ziel der (meist operativen) Behandlung ist die Verbesserung der Lebensqualität über eine effiziente Schmerzlinderung, möglichst durch eine einzige Operation mit kurzer Hospitalisationszeit. Für die chirurgische Behandlung sollten im proximalen Abschnitt des Femurs Prothesen verwendet werden, bei subtrochantären und Schaftfrakturen vornehmlich intramedulläre Kraftträger. Eine postoperative Radiotherapie scheint die lokale Tumorprogression zu verhindern. Bei guter Langzeitprognose sollte der Tumor lokal aggressiv ausgeräumt werden.

Spermatogenesis and testicular tumours in ageing dogs

Reproduction ◽  
2000 ◽  
pp. 443-452 ◽  
Author(s):  
MA Peters ◽  
DG de Rooij ◽  
KJ Teerds ◽  
I van Der Gaag ◽  
FJ van Sluijs
Keyword(s):  
Sertoli Cell ◽  
Leydig Cell ◽  
Seminiferous Tubules ◽  
Score System ◽  
Testicular Tumours ◽  
Severe Impairment ◽  
Per Se ◽  
Testicular Disease ◽  
Sertoli Cell Tumours ◽  
Leydig Cell Tumours

Spermatogenesis was examined in testes from 74 dogs of various breeds without clinically detected testicular disease. A modified Johnsen score system was used to determine whether spermatogenesis deteriorates with ageing. The diameter of seminiferous tubules was measured in dogs without testicular disease to examine other possible effects of ageing on tubular performance. There appeared to be no relation between age and these variables. The influence of testicular tumours on spermatogenesis was also investigated in both affected and unaffected testes. The testes of 28 dogs with clinically palpable tumours and 21 dogs with clinically non-palpable tumours were investigated. In cases of unilateral occurrence of a tumour, impairment of spermatogenesis was observed only in the affected testis of dogs with clinically detected tumours. Bilateral occurrence of tumours, whether detected clinically or non-clinically, was associated with severe impairment of spermatogenesis. The prevalence of tumours increased during ageing. Eighty-six per cent of the clinically detected and 57% of the non-clinically detected tumours were found in old dogs. Multiple types of tumour and bilateral occurrence were very common. Seminomas and Leydig cell tumours were more frequent than Sertoli cell tumours. It was concluded that spermatogenesis per se did not decrease during ageing in dogs but the occurrence of testicular tumours increased with ageing and affected spermatogenesis significantly, as reflected by a lower Johnsen score.

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