Collaborative Filtering Recommender Systems Contents
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The Use of Time Dimension in Recommender Systems for Learning
The Use of Time Dimension in Recommender Systems for Learning Eduardo José de Borba1, Isabela Gasparini1 and Daniel Lichtnow2 1Graduate Program in Applied Computing (PPGCA), Department of Computer Science (DCC), Santa Catarina State University (UDESC), Paulo Malschitzki 200, Joinville, Brazil 2Polytechnic School, Federal University of Santa Maria (UFSM), Av. Roraima 1000, Santa Maria, Brazil Keywords: Recommender System, Context-aware, Time, Learning. Abstract: When the amount of learning objects is huge, especially in the e-learning context, users could suffer cognitive overload. That way, users cannot find useful items and might feel lost in the environment. Recommender systems are tools that suggest items to users that best match their interests and needs. However, traditional recommender systems are not enough for learning, because this domain needs more personalization for each user profile and context. For this purpose, this work investigates Time-Aware Recommender Systems (Context-aware Recommender Systems that uses time dimension) for learning. Based on a set of categories (defined in previous works) of how time is used in Recommender Systems regardless of their domain, scenarios were defined that help illustrate and explain how each category could be applied in learning domain. As a result, a Recommender System for learning is proposed. It combines Content-Based and Collaborative Filtering approaches in a Hybrid algorithm that considers time in Pre- Filtering and Post-Filtering phases. 1 INTRODUCTION potential to improve the quality of recommendation (Campos et al., 2014). Nowadays, there are distinct educational approaches, This work aims to identify how Time-aware RS e.g. online learning, blended learning, face-to-face (Context-aware RS that uses time context) can be learning, etc. -
CRUC: Cold-Start Recommendations Using Collaborative Filtering in Internet of Things
CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things Daqiang Zhanga,*, Qin Zoub, Haoyi Xiongc a School of Computer Science, Nanjing Normal University, Nanjing, Jiangsu Province, 210046,China b School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei Province, 430072, China c Department of Telecommunication, Institute Telecome – Telecom SudParis, Evry, 91011, France Abstract The Internet of Things (IoT) aims at interconnecting everyday objects (including both things and users) and then using this connection information to provide customized user services. However, IoT does not work in its initial stages without adequate acquisition of user preferences. This is caused by cold-start problem that is a situation where only few users are interconnected. To this end, we propose CRUC scheme --- Cold-start Recommendations Using Collaborative Filtering in IoT, involving formulation, filtering and prediction steps. Extensive experiments over real cases and simulation have been performed to evaluate the performance of CRUC scheme. Experimental results show that CRUC efficiently solves the cold-start problem in IoT. Keywords: Cold-start Problem, Internet of Things, Collaborative Filtering 1. Introduction The Internet of Things (IoT) refers to a self-configuring network in which everyday objects are interconnected to the Internet [1] [2]. IoT deploys sensors in infrastructures (e.g., rooms and buildings) to get a heightened awareness of real-time events. It also employs sensors capturing contextual information about objects (e.g., user preferences) to achieve an enhanced situational awareness [3] [21-26]. Readings from a large number of sensors for various objects are enormous, but only a few of them are useful for a specific user. -
Critical Thinking STARS Handout Finalx
Critical Thinking "It is the mark of an educated mind to be able to entertain a thought without accepting it." - Aristotle "The important thing is not to stop questioning." - Albert Einstein What is critical thinking? Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action. In its exemplary form, it is based on universal intellectual values that transcend subject matter divisions: clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness. A Critical Thinker: Asks pertinent questions Assess statements and arguments Is able to admit a lack of understanding or information Has a sense of curiosity Is interested in finding new solutions Examines beliefs, assumptions and opinions and weighs them against facts Listens carefully to others and can give effective feedback Suspends judgment until all facts have been gathered and considered Look for evidence to support assumptions or beliefs Is able to adjust beliefs when new information is found Examines problems closely Is able to reject information that is irrelevant or incorrect Critical Thinking Standards and Questions: The most significant thinking (intellectual) standards/questions: • Clarity o Could you elaborate further on that point? o Could you give me an example? o Could you express -
Critical Thinking for the Military Professional
Document created: 17 Jun 04 Critical Thinking For The Military Professional Col W. Michael Guillot “Any complex activity, if it is to be carried on with any degree of virtuosity, calls for appropriate gifts of intellect and temperament …Genius consists in a harmonious combination of elements, in which one or the other ability may predominate, but none may be in conflict with the rest.”1 In a previous article on Strategic leadership I described the strategic environment as volatile, uncertain, complex, and ambiguous (VUCA). Additionally, that writing introduced the concept of strategic competency.2 This article will discuss the most important essential skill for Strategic Leaders: critical thinking. It is hard to imagine a Strategic leader today who does not think critically or at least uses the concept in making decisions. Critical thinking helps the strategic leader master the challenges of the strategic environment. It helps one understand how to bring stability to a volatile world. Critical thinking leads to more certainty and confidence in an uncertain future. This skill helps simplify complex scenarios and brings clarity to the ambiguous lens. Critical thinking is the kind of mental attitude required for success in the strategic environment. In essence, critical thinking is about learning how to think and how to judge and improve the quality of thinking—yours and others. Lest you feel you are already a great critical thinker, consider this, in a recent study supported by the Kellogg Foundation, only four percent of the U.S. organizational -
Recent Work in Relevant Logic
Recent Work in Relevant Logic Mark Jago Forthcoming in Analysis. Draft of April 2013. 1 Introduction Relevant logics are a group of logics which attempt to block irrelevant conclusions being drawn from a set of premises. The following inferences are all valid in classical logic, where A and B are any sentences whatsoever: • from A, one may infer B → A, B → B and B ∨ ¬B; • from ¬A, one may infer A → B; and • from A ∧ ¬A, one may infer B. But if A and B are utterly irrelevant to one another, many feel reluctant to call these inferences acceptable. Similarly for the validity of the corresponding material implications, often called ‘paradoxes’ of material implication. Relevant logic can be seen as the attempt to avoid these ‘paradoxes’. Relevant logic has a long history. Key early works include Anderson and Belnap 1962; 1963; 1975, and many important results appear in Routley et al. 1982. Those looking for a short introduction to relevant logics might look at Mares 2012 or Priest 2008. For a more detailed but still accessible introduction, there’s Dunn and Restall 2002; Mares 2004b; Priest 2008 and Read 1988. The aim of this article is to survey some of the most important work in the eld in the past ten years, in a way that I hope will be of interest to a philosophical audience. Much of this recent work has been of a formal nature. I will try to outline these technical developments, and convey something of their importance, with the minimum of technical jargon. A good deal of this recent technical work concerns how quantiers should work in relevant logic. -
Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation
Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation Daniel Valcarce, Javier Parapar, and Álvaro Barreiro Information Retrieval Lab Computer Science Department University of A Coruña, Spain {daniel.valcarce,javierparapar,barreiro}@udc.es Abstract. Recently, Relevance-Based Language Models have been dem- onstrated as an effective Collaborative Filtering approach. Nevertheless, this family of Pseudo-Relevance Feedback techniques is computationally expensive for applying them to web-scale data. Also, they require the use of smoothing methods which need to be tuned. These facts lead us to study other similar techniques with better trade-offs between effective- ness and efficiency. Specifically, in this paper, we analyse the applicability to the recommendation task of four well-known query expansion tech- niques with multiple probability estimates. Moreover, we analyse the ef- fect of neighbourhood length and devise a new probability estimate that takes into account this property yielding better recommendation rank- ings. Finally, we find that the proposed algorithms are dramatically faster than those based on Relevance-Based Language Models, they do not have any parameter to tune (apart from the ones of the neighbourhood) and they provide a better trade-off between accuracy and diversity/novelty. Keywords: Recommender Systems, Collaborative Filtering, Query Expansion, Pseudo-Relevance Feedback. 1 Introduction Recommender systems are recognised as a key instrument to deliver relevant information to the users. Although the problem that attracts most attention in the field of Recommender Systems is accuracy, the emphasis on efficiency is increasing. We present new Collaborative Filtering (CF) algorithms. CF methods exploit the past interactions betweens items and users. Common approaches to CF are based on nearest neighbours or matrix factorisation [17]. -
Combining User-Based and Item-Based Collaborative Filtering Using Machine Learning
Combining User-Based and Item-Based Collaborative Filtering Using Machine Learning Priyank Thakkar, Krunal Varma, Vijay Ukani, Sapan Mankad and Sudeep Tanwar Abstract Collaborative filtering (CF) is typically used for recommending those items to a user which other like-minded users preferred in the past. User-based collaborative filtering (UbCF) and item-based collaborative filtering (IbCF) are two types of CF with a common objective of estimating target user’s rating for the target item. This paper explores different ways of combining predictions from UbCF and IbCF with an aim of minimizing overall prediction error. In this paper, we propose an approach for combining predictions from UbCF and IbCF through multiple linear regression (MLR) and support vector regression (SVR). Results of the proposed approach are compared with the results of other fusion approaches. The comparison demonstrates the superiority of the proposed approach. All the tests are performed on a large publically available dataset. Keywords User-based collaborative filtering · Item-based collaborative filtering Machine learning · Multiple linear regression · Support vector regression P. Thakkar (B) · K. Varma · V. Ukani · S. Mankad · S. Tanwar Institute of Technology, Nirma University, Ahmedabad 382481, India e-mail: [email protected] K. Varma e-mail: [email protected] V. Ukani e-mail: [email protected] S. Mankad e-mail: [email protected] S. Tanwar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 173 S. C. Satapathy and A. Joshi (eds.), Information and Communication Technology for Intelligent Systems, Smart Innovation, Systems and Technologies 107, https://doi.org/10.1007/978-981-13-1747-7_17 174 P. -
A Grouplens Perspective
From: AAAI Technical Report WS-98-08. Compilation copyright © 1998, AAAI (www.aaai.org). All rights reserved. RecommenderSystems: A GroupLensPerspective Joseph A. Konstan*t , John Riedl *t, AI Borchers,* and Jonathan L. Herlocker* *GroupLensResearch Project *Net Perceptions,Inc. Dept. of ComputerScience and Engineering 11200 West78th Street University of Minnesota Suite 300 Minneapolis, MN55455 Minneapolis, MN55344 http://www.cs.umn.edu/Research/GroupLens/ http://www.netperceptions.com/ ABSTRACT identifying sets of articles by keyworddoes not scale to a In this paper, wereview the history and research findings of situation in which there are thousands of articles that the GroupLensResearch project I and present the four broad contain any imaginable set of keywords. Taken together, research directions that we feel are most critical for these two weaknesses represented an opportunity for a new recommender systems. type of filtering, that would focus on finding which INTRODUCTION:A History of the GroupLensProject available articles matchhuman notions of quality and taste. The GroupLens Research project began at the Computer Such a system would be able to produce a list of articles Supported Cooperative Work (CSCW)Conference in 1992. that each user wouldlike, independentof their content. Oneof the keynote speakers at the conference lectured on a Wedecided to apply our ideas in the domain of Usenet his vision of an emerging information economy,in which news. Usenet screamsfor better information filtering, with most of the effort in the economywould revolve around hundreds of thousands of articles posted daily. Manyof the production, distribution, and consumptionof information, articles in each Usenet newsgroupare on the sametopic, so rather than physical goods and services. -
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks
342 Computational Advantages of Relevance Reasoning in Bayesian Belief Networks Yan Lin Marek J. Druzdzel University of Pittsburgh University of Pittsburgh Intelligent Systems Program Department of Information Science Pittsburgh, PA 1.5260 and Intelligent Systems Program [email protected] Pittsburgh, PA 15260 marek@sis. pitt. edu Abstract reasoning, shown to be NP-hard both for exact infer ence [Cooper, 1990] and for approximate [Dagurn. and This paper introduces a computational Luby, 1993] inference. framework for reasoning in Bayesian belief The critical factor in exact inference schemes is the networks that derives significant advantages topology of the underlying graph and, more specifi from focused inference and relevance reason cally, its connectivity. The complexity of approximate ing. This framework is based on d-separation schemes may, in addition, depend on factors like the and other simple and computationally effi a-priori likelihood of the observed evidenee or asymme cient techniques for pruning irrelevant parts tries in probability distributions. There are a number of a network. Our main contribution is a of ingeniously efficient algorithms that allow for fast technique that we call relevance-based decom belief updating in moderately sized models.2 Still, position. Relevance-based decomposition ap eaeh of them is subject to the growth in complexity proaches belief updating in large networks that is generally exponential in the size of the model. by focusing on their parts and decompos Given the promise of the decision-theoretic approach ing them into partially overlapping subnet and an increasing number of its practical applications, works. This makes reasoning in some in it is important to develop schemes that will reduce the tractable networks possible and, in addition, computational complexity of inference. -
John P. Burgess Department of Philosophy Princeton University Princeton, NJ 08544-1006, USA [email protected]
John P. Burgess Department of Philosophy Princeton University Princeton, NJ 08544-1006, USA [email protected] LOGIC & PHILOSOPHICAL METHODOLOGY Introduction For present purposes “logic” will be understood to mean the subject whose development is described in Kneale & Kneale [1961] and of which a concise history is given in Scholz [1961]. As the terminological discussion at the beginning of the latter reference makes clear, this subject has at different times been known by different names, “analytics” and “organon” and “dialectic”, while inversely the name “logic” has at different times been applied much more broadly and loosely than it will be here. At certain times and in certain places — perhaps especially in Germany from the days of Kant through the days of Hegel — the label has come to be used so very broadly and loosely as to threaten to take in nearly the whole of metaphysics and epistemology. Logic in our sense has often been distinguished from “logic” in other, sometimes unmanageably broad and loose, senses by adding the adjectives “formal” or “deductive”. The scope of the art and science of logic, once one gets beyond elementary logic of the kind covered in introductory textbooks, is indicated by two other standard references, the Handbooks of mathematical and philosophical logic, Barwise [1977] and Gabbay & Guenthner [1983-89], though the latter includes also parts that are identified as applications of logic rather than logic proper. The term “philosophical logic” as currently used, for instance, in the Journal of Philosophical Logic, is a near-synonym for “nonclassical logic”. There is an older use of the term as a near-synonym for “philosophy of language”. -
Towards an Effective Crowdsourcing Recommendation System a Survey of the State-Of-The-Art
2015 IEEE Symposium on Service-Oriented System Engineering Towards an Effective Crowdsourcing Recommendation System A Survey of the State-of-the-Art Eman Aldhahri, Vivek Shandilya, Sajjan Shiva Computer Science Department, The University of Memphis Memphis, USA {aldhahri, vmshndly, sshiva}@memphis.edu Abstract—Crowdsourcing is an approach where requesters can behaviors. User behavior can be interpreted as preferable call for workers with different capabilities to process a task for features.. Explicit profiles are based on asking users to complete monetary reward. With the vast amount of tasks posted every a preferable features form. Two techniques common in this area day, satisfying workers, requesters, and service providers--who are: a collaborative filtering approach (with cold start problems, are the stakeholders of any crowdsourcing system--is critical to scarcity and scalability in large datasets [6]), and a content- its success. To achieve this, the system should address three based approach (with problems due to overspecialization, etc.). objectives: (1) match the worker with a suitable task that fits the A hybrid approach that combines the two techniques has also worker’s interests and skills, and raise the worker’s rewards; (2) been used. give requesters more qualified solutions with lower cost and II. MOTIVATION time; and (3) raise the accepted tasks rate which will raise the The main objective in this study is to investigate various online aggregated commissions accordingly. For these objectives, we recommendation systems by analyzing their input parameters, present a critical study of the state-of-the-art in effectiveness, and limitations in order to assess their usage in recommendation systems that are ubiquitous among crowdsourcing systems. -
Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 2 January 2020 doi:10.20944/preprints202001.0015.v1 Article Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction Qinglong Li 1, Ilyoung Choi 2 and Jaekyeong Kim 3,* 1 Department of Social Network Science, KyungHee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02453, Republic of Korea; [email protected] 2 Graduate School of Business Administration, KyungHee University, 26, Kyungheedae-ro, Dongdaemun- gu, Seoul, 02453, Republic of Korea; [email protected] 3 School of Management, KyungHee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02453, Republic of Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-961-9355 (F.L.) Abstract: With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service.