Distributed Recommender Systems for Internet Commerce

2005 ◽  
pp. 907-911 ◽  
Author(s):  
Badrul M. Sarwar ◽  
Joseph A. Konstan ◽  
John T. Riedl
Keyword(s):  
Recommender Systems ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Weighted Average ◽  
Research Literature ◽  
Human Beings ◽  
Internet Commerce ◽  
Rule Based ◽  
Evaluation Approach ◽  
The Many

Recommender systems (RSs) present an alternative information-evaluation approach based on the judgements of human beings (Resnick & Varian, 1997). It attempts to automate the word-of-mouth recommendations that we regularly receive from family, friends, and colleagues. In essence, it allows everyone to serve as a critic. This inclusiveness circumvents the scalability problems of individual critics—with millions of readers it becomes possible to review millions of books. At the same time it raises the question of how to reconcile the many and varied opinions of a large community of ordinary people. Recommender systems address this question through the use of different algorithms: nearest-neighbor algorithms (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994; Shardanand et al., 1994), item-based algorithms (Sarwar, Karypis, Konstan, & Riedl, 2001), clustering algorithms (Ungar & Foster, 1998), and probabilistic and rule-based learning algorithms (Breese, Heckerman, & Kadie, 1998), to name but a few. The nearest-neighbor-algorithm-based recommender systems, which are often referred to as collaborative filtering (CF) systems in research literature (Maltz & Ehrlich, 1995), are the most widely used recommender systems in practice. A typical CF-based recommender system maintains a database containing the ratings that each customer has given to each product that customer has evaluated. For each customer in the system, the recommendation engine computes a neighborhood of other customers with similar opinions. To evaluate other products for this customer, the system forms a normalized and weighted average of the opinions of the customer’s neighbors.

scholarly journals Ensemble Based Hybrid Recommender Systems

2020 ◽  
Vol 9 (3) ◽  
pp. 826-833
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Nearest Neighbor ◽  
Demographic Data ◽  
Weighted Average ◽  
Cold Start ◽  
User Preferences ◽  
Vector Method ◽  
Hybrid Recommender Systems ◽  
Hybrid Recommender

In the past few years, the advent of computational and prediction technologies has spurred a lot of interest in recommendation research. Content-based recommendation and collaborative filtering are two elementary ways to build recommendation systems. In a content based recommender system, products are described using keywords and a user profile is developed to enlist the type of products the user may like. Widely used Collaborative filtering recommender systems provide recommendations based on similar user preferences. Hybrid recommender systems are a blend of content-based and collaborative techniques to harness their advantages to maximum. Although both these methods have their own advantages, they fail in ‘cold start’ situations where new users or products are introduced to the system, and the system fails to recommend new products as there is no usage history available for these products. In this work we work on MovieLens 100k dataset to recommend movies based on the user preferences. This paper proposes a weighted average method for combining predictions to improve the accuracy of hybrid models. We used standard error as a measure to assign the weights to the classifiers to approximate their participation in predicting the recommendations. The cold start problem is addressed by including demographic data of the user by using three approaches namely Latent Vector Method, Bayesian Weighted Average, and Nearest Neighbor Algorithm.

DRSA: a non-hierarchical clustering algorithm using k-NN graph and its application in vegetation classification

2015 ◽  
pp. 125-138 ◽  
Author(s):  
I. V. Goncharenko
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Hierarchical Cluster ◽  
Vegetation Classification ◽  
K Nearest Neighbor ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph

In this article we proposed a new method of non-hierarchical cluster analysis using k-nearest-neighbor graph and discussed it with respect to vegetation classification. The method of k-nearest neighbor (k-NN) classification was originally developed in 1951 (Fix, Hodges, 1951). Later a term “k-NN graph” and a few algorithms of k-NN clustering appeared (Cover, Hart, 1967; Brito et al., 1997). In biology k-NN is used in analysis of protein structures and genome sequences. Most of k-NN clustering algorithms build «excessive» graph firstly, so called hypergraph, and then truncate it to subgraphs, just partitioning and coarsening hypergraph. We developed other strategy, the “upward” clustering in forming (assembling consequentially) one cluster after the other. Until today graph-based cluster analysis has not been considered concerning classification of vegetation datasets.

The Measure of Greatness

2019 ◽  
Keyword(s):  
Good Life ◽  
Natural World ◽  
The Good Life ◽  
Human Beings ◽  
The Social ◽  
Moral Nature ◽  
Complex Trajectory ◽  
The Many ◽  
Human Excellence ◽  
Ethical Perspectives

Magnanimity is a virtue that has led many lives. Foregrounded early on by Plato as the philosophical virtue par excellence, it became one of the crown jewels in Aristotle’s account of human excellence and was accorded an equally salient place by other ancient thinkers. One of the most distinctive elements of the ancient tradition to filter into the medieval Islamic and Christian worlds, it sparked important intellectual engagements there and went on to carve deep tracks through several later philosophies that inherited from this tradition. Under changing names, under reworked forms, it continued to breathe in the thought of Descartes and Hume, Kant and Nietzsche, and their successors. Its many lives have been joined by important continuities. Yet they have also been fragmented by discontinuities—discontinuities reflecting larger shifts in ethical perspectives and competing answers to questions about the nature of the good life, the moral nature of human beings, and their relationship to the social and natural world they inhabit. They have also been punctuated by moments of controversy in which the greatness of this vision of human greatness has itself been called into doubt. This volume provides a window to the complex trajectory of a virtue whose glitter has at times been as heady as it has been divisive. By exploring the many lives it has lived, we will be in a better position to decide whether and why this is a virtue we might still want to make central to our own ethical lives.

scholarly journals Context-aware recommender systems and cultural heritage: a survey

2021 ◽  
Author(s):  
Mario Casillo ◽  
Francesco Colace ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender Systems ◽  
Context Awareness ◽  
Technological Development ◽  
Context Aware ◽  
Human Beings ◽  
Context Analysis ◽  
Analysis Techniques ◽  
The Right ◽  
Technological Improvements

AbstractIn the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

scholarly journals STOUT: SMILES to IUPAC names using neural machine translation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Machine Translation ◽  
Similarity Index ◽  
Chemical Compounds ◽  
Human Beings ◽  
International Union ◽  
Rule Based ◽  
Neural Machine Translation ◽  
Tanimoto Similarity ◽  
Reverse Translation ◽  
String Representation

AbstractChemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International Union of Pure and Applied Chemistry (IUPAC) based on a set of rules. Due to the complexity of this ruleset a correct chemical name assignment remains challenging for human beings and there are only a few rule-based cheminformatics toolkits available that support this task in an automated manner. Here we present STOUT (SMILES-TO-IUPAC-name translator), a deep-learning neural machine translation approach to generate the IUPAC name for a given molecule from its SMILES string as well as the reverse translation, i.e. predicting the SMILES string from the IUPAC name. In both cases, the system is able to predict with an average BLEU score of about 90% and a Tanimoto similarity index of more than 0.9. Also incorrect predictions show a remarkable similarity between true and predicted compounds.

scholarly journals Using Matrix-Product States for Open Quantum Many-Body Systems: Efficient Algorithms for Markovian and Non-Markovian Time-Evolution

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 984
Author(s):  
Regina Finsterhölzl ◽  
Manuel Katzer ◽  
Andreas Knorr ◽  
Alexander Carmele
Keyword(s):  
Time Evolution ◽  
Nearest Neighbor ◽  
Matrix Product ◽  
Body System ◽  
Many Body ◽  
Initial State ◽  
Matrix Product States ◽  
Open Quantum ◽  
Many Body Systems ◽  
The Many

This paper presents an efficient algorithm for the time evolution of open quantum many-body systems using matrix-product states (MPS) proposing a convenient structure of the MPS-architecture, which exploits the initial state of system and reservoir. By doing so, numerically expensive re-ordering protocols are circumvented. It is applicable to systems with a Markovian type of interaction, where only the present state of the reservoir needs to be taken into account. Its adaption to a non-Markovian type of interaction between the many-body system and the reservoir is demonstrated, where the information backflow from the reservoir needs to be included in the computation. Also, the derivation of the basis in the quantum stochastic Schrödinger picture is shown. As a paradigmatic model, the Heisenberg spin chain with nearest-neighbor interaction is used. It is demonstrated that the algorithm allows for the access of large systems sizes. As an example for a non-Markovian type of interaction, the generation of highly unusual steady states in the many-body system with coherent feedback control is demonstrated for a chain length of N=30.

scholarly journals User involvement in adolescents’ mental healthcare: protocol for a systematic review

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e018800
Author(s):  
Petter Viksveen ◽  
Stig Erlend Bjønness ◽  
Siv Hilde Berg ◽  
Nicole Elizabeth Cardenas ◽  
Julia Rose Game ◽  
...  
Keyword(s):  
Systematic Review ◽  
Critical Appraisal ◽  
Research Evidence ◽  
Data Extraction ◽  
Mental Healthcare ◽  
User Involvement ◽  
Cochrane Collaboration ◽  
Research Literature ◽  
Primary Data ◽  
Evaluation Approach

IntroductionUser involvement has become a growing importance in healthcare. The United Nations state that adolescents have a right to be heard, and user involvement in healthcare is a legal right in many countries. Some research provides an insight into the field of user involvement in somatic and mental healthcare for adults, but little is known about user involvement in adolescents’ mental healthcare, and no overview of the existing research evidence exists.Methods and analysisThe aim of this systematic review is to provide an overview of existing research reporting on experiences with and the effectiveness and safety issues associated with user involvement for adolescents’ mental healthcare at the individual and organisational level. A systematic literature search and assessment of published research in the field of user involvement in adolescents’ mental healthcare will be carried out. Established guidelines will be used for data extraction (Cochrane Collaboration guidelines, Strengthening the Reporting of Observational studies in Epidemiology and Critical Appraisal Skills Programme (CASP)), critical appraisal (Cochrane Collaboration guidelines and Pragmatic-Explanatory Continuum Indicator Summary) and reporting of results (Preferred Reporting Items for Systematic reviews and Meta-Analyses, Consolidated Standards of Reporting Trials and CASP). Confidence in the research evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. Adolescents are included as coresearchers for the planning and carrying out of this systematic review. This systematic review will provide an overview of the existing research literature and thereby fill a knowledge gap. It may provide various stakeholders, including decision-makers, professionals, individuals and their families, with an overview of existing knowledge in an underexplored field of research.Ethics and disseminationEthics approval is not required for this systematic review as we are not collecting primary data. The results will be published in a peer-reviewed journal and at conference presentations and will be shared with stakeholder groups.

An improved OPTICS clustering algorithm for discovering clusters with uneven densities

2021 ◽  
Vol 25 (6) ◽  
pp. 1453-1471
Author(s):  
Chunhua Tang ◽  
Han Wang ◽  
Zhiwen Wang ◽  
Xiangkun Zeng ◽  
Huaran Yan ◽  
...  
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Substantial Improvement ◽  
Experimental Results ◽  
High Time ◽  
Parameter Setting ◽  
K Nearest Neighbor ◽  
Density Based Clustering

Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.

The Chronology of Sudanese Arabic Genealogical Tradition

2000 ◽  
Vol 27 ◽  
pp. 325-337 ◽  
Author(s):  
Jay Spaulding
Keyword(s):  
Nineteenth Century ◽  
World War I ◽  
Historical Record ◽  
Economic Transformation ◽  
Nile Valley ◽  
Human Beings ◽  
Old Kingdom ◽  
History Of ◽  
The Many ◽  
Islamic Faith

Modern nationalisms first arose during the later eighteenth century around the wide periphery of the ancient heartland of western culture and gnawed their way inward during the course of the nineteenth century to the core, culminating in World War I, Each new nationalism generated an original “imagined community” of human beings, part of whose ideological cohesion derived from a sense of shared historical experience. Since the actual historical record would not necessarily satisfy this hunger, it was often found expedient to amend the past through acts of imagination aptly termed the “invention of tradition.”One of the many new “imagined communities” of the long nineteenth century took shape in the northern Nile-valley Sudan between the final disintegration of the old kingdom of Sinnar (irredeemable after the death of the strongman Muhammad Abu Likaylik in 1775) and the publication of Harold MacMichael's A History of the Arabs in the Sudan in 1922. The new national community born of the collapse of Sinnar, strongly committed to Arabic speech and Islamic faith, was tested by fire through foreign conquest and revolution, by profound socio-economic transformation, and by the challenges attendant on participation in an extended sub-imperialism that earned it hegemony—first cultural, and ultimately political—over all the diverse peoples of the modern Sudan.One important response of the nascent community to the trials of this difficult age was the invention of a new national historical tradition, according to which its members were descended via comparatively recent immigrants to the Sudan from eminent Arabs of Islamic antiquity.

scholarly journals A novel content based image retrieval system using K-means/KNN with feature extraction

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Color Analysis ◽  
Image Retrieval System ◽  
First Time ◽  
System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

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