This article has been made free for everyone, thanks to Medium Members. Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) are two popular approaches to compute matrix factorization, and there has . Deep Matrix Factorization Approach for Collaborative MDPI. <> 9 0 obj 1.Matrix Factorization Techniques For Recommender SystemsReporter: Lei Guohttp://ir.sdu.edu.cn/index.htmCopyright 2012 by IRLAB@SDU-1-2. The rest of this paper is structured as follows. Neighborhood Methods computing the relationships between items or, alternatively, between users (user base), Latent Factor creates a latent feature to compare one user to another user (feature base). (If new user exist and does not have any inter-dependencies among others, we cant recommend anything). Section 2 introduces the matrix factorization in recommender system. The recent model PREFER (Guo et al., 2021) is a sequence-based matrix factorization recommender system designed for the POI domain: the training data is enriched with time and distance information and, similarly to our model, does not share sensitive parameters about user profiles. 4- Understanding matrix factorization for recommendation. All .ipynb files are standalone and do not require importing mf.py Since matrix factorization in recommender systems is an active research field, there are numerous recommendation algorithms based on it. En.wikipedia.org. endobj In general there is two method to achieve Collaborative filtering. In particular, we study the differences in the embeddings and the recommendations produced by the same model under different . *7*'{Z@k3/gnuEquI2lv` W= r&YD*1pOuJ~iV5UX{#JT-UMJR#gZZL*IYQgee 19 0 obj Retrieved 22 November 2018, from. <> endobj Datajobs.com. Social science approaches to missing values predict avoided, unrequested, or lost information from dense data sets, typically surveys. 6 0 obj The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task. In this tutorial, we will go through the basic ideas and the mathematics of matrix factorization, and then we will present a simple implementation in Python. 1 0 obj Bernoulli Matrix Factorization (BeMF) [25] and the federated. Matrix factorization is a strong technique that has been used widely as the base algorithm in recommendation systems [9, 19]. 14 0 obj Recommendation algorithms can be either based on content or driven by collaborative filtering. View 11 excerpts, cites background and methods. (or even confident scores). This work adopts three different types of MF algorithms: regularized MF, maximum margin MF and non-negative MF for the Netflix Prize competition, and instead of selecting the optimal parameters, combines the results obtained with several parameters. But the error surface is not convex, we can also take the alternative approach in which we fix q and p alternatively while optimizing for another. <> endobj Source: Matrix Factorization Techniques for Recommender Systems-Koren and VolinskyMatrix Factorization: Extract latent factors that characterizes the users and items. The above image does an excellent job of summarizing, the core idea behind matrix factorization. Content Filtering creates a profile for each user or product to characterize its nature (Success Case: Music Genome Project), Collaborative Filtering analyzes relationships between users and inter-dependencies among products to identify new user-item associations (Success Case: Tapestry). Download Free PDF. <> Matrix Factorization ? Since our goal is to generalize the previous ratings in a way that predicts future, unknown ratings, we should not over-fit our model. A novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata, achieves better prediction accuracy than some of the popular state-of-the-art MF-based recommendation models. <> Matrix factorization (MF) algorithms are variants of latent factor models, which are easy, fast, and efficient. 17 0 obj Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online . Since we have the loss function defined, take the partial derivative respect to q and p to optimize those values. Medium. Content Filtering creates a profile for each user or product to characterize its nature (Success Case: Music Genome Project). 6- A Gentle Introduction to Matrix Factorization for Machine . <> An improved recommendation method based on item-diversity, adding bias and implicit feedback is proposed, which significantly improves the diversity of recommendations and accuracy also be improved to some extent with diversity growing. In this paper the authors used matrix factorization technique to build a sophisticated recommender system in which outperformed nearest-neighbor techniques. It's the computerized alternative to the "genes" in pandora's music genome project. 10 0 obj A Medium publication sharing concepts, ideas and codes. a Basic matRix factoRization modeL Matrix factorization models map both users and items to a joint latent factor space of dimensionality f, such that user-item interactions are modeled as inner products in that space. To recommendation techniques for factorization recommender systems. endobj Accordingly, each item i is associated with a Gear ed toward males Serious Escapist Diaries . However, most collaborative filtering algorithms suffer from data sparsity. Those algorithms encompass several extensions, e.g. Section 3 introduces sometraditional algorithms. As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Experimental results show that the proposed attributes coupling based item enhanced matrix factorization method outperforms state-of-the-art recommendation algorithms and can effectively cope with the cold start item problem when more item attribute information is available. We will proceed with the assumption that we are dealing with user ratings (e.g. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when . PDF | Due to the huge amount of multimedia data and the backwardness of traditional data management technology for multimedia data management, it is. [1] <> Download Free PDF . 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). Although sequential models employ the same metrics as the other . Your home for data science. endobj ML Matrix Factorization recommender. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. recommender_final_toy_dataset.py shows how exactly Matrix Factorization Techniques work by considering a 5x5 toy dataset. endobj One of the most efficient techniques making recommender systems successful is matrix factorization. And though these factorization based techniques work extremely well, there's research being done on new methods. "kmiNizhj+9Is&F5v_e3|Y\ >;e ddd7G(bak\5}"*3Usr/_Qfkvwl6[kko>G^Rh,{_5of6g8y'{k|z|],*NYFy": endobj And if that estimated rating is high, we can recommend that movie to the user. This paper takes a top-down view of recommender systems and uses eBay as an example where the elaborate interaction offers a number opportunities for creative recommendations, however, eBay also poses complexities resulting from high sparsity of relationships. MF basically captures the characteristics of users and items through factor vectors inferred from rating patterns. xMo@~EQQDj*81DM;k M9cwvy+%.sv)0B0V0AX[c3 I think it is super cool that we can use matrix factorization to build recommendation system. Recommender system strategies. endobj Collaborative Filtering analyzes relationships between users and inter-dependencies among products to identify new user-item associations (Success Case: Tapestry). nonnegative matrix factorization (NMF) [4]. We also have N people whom we'll call users of our recommender system. Just as a side note, the above concept is heavily related to Singular Value Decomposition (SVD). When a user gives feed back to a certain movie they saw (say they can rate from one to five), this collection of feedback can be represented in a form of a matrix. We will proceed with the assumption that we are dealing with user ratings (e.g. And the new objective function would look something like below. Experimental results show that the proposed attributes coupling based matrix factorization method outperforms the state-of-the-art recommendation algorithms and can effectively cope with the cold start item problem when such item-attribute information is available. % Picture courtesy: Matrix Factorization Techniques for Recommender Systems: Koren et al, 2009 Probabilistic Machine Learning (CS772A) Probabilistic Matrix Factorization 5. The complexity of the factorization is O(n3), although in some cases on sparse graphs we can obtain speedups to O(n2). gB 6Ljt._UpIS>GFY[r,NHbIkK;?l-NJPk) qB{JU'Ci P)_y | >|lq8_sQ,ckb)h&hKstXw3l3tpL3*pl)Mcea$'#j:o>G\D|5bMw Z7- x iqm 8eTg$\|jxU/OVeU\` |/Oh| Eze%|RbcP*MRn7]^.w,R+k endobj However, we still likely lose some meaningful signals by using a lower-rank matrix. It includes an assignment and quiz (both due in the second week), and an honors assignment (also due in the second week). 5- Matrix Factorization: A Simple Tutorial and Implementation in Python. In section 4, we propose an improved algorithm. This is a two-part, two-week module on matrix factorization recommender techniques. Matrix Factorization as Feature Engineering in Recommender Systems User Item data set decomposed into User and Item Matrices(figure 3) Suppose we have a data set which contains the items . This work uses the publicly available user generated information contained in Wikipedia to identify similarities between items by mapping them to Wikipedia pages and finding similarities in the text and commonalities in the links and categories of each page to improve ranking predictions. These efforts have resulted in various types probabilistic matrix factorization (which works and scales even better) and many other approaches. The matrix factorization method are widely used in the latent factor model to find the high-expected rated items and hence highly favoured items by the active user. As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of. One obvious method to find matrix q and p is the gradient descent method. A novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata, achieves better prediction accuracy than some of the popular state-of-the-art MF-based recommendation models. These are known as biases or intercepts, and they are independent of any interactions, and it wouldnt be wise to explain these bias terms using our two decomposing matrix q and p. So we include the bias terms into our original equation. View 3 excerpts, references methods and background, By clicking accept or continuing to use the site, you agree to the terms outlined in our. A privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest to allow a form of "hiding in the crowd" privacy and demonstrates that the increased privacy does not come at the cost of reduced recommendation accuracy. Some movies are bias in that it is widely perceived better (or worse) than other movies, and some users are bias in that they are super salty and never rate a movie greater than 2. <> .64W'y-|"rO\ ^WthNrtL]1G%>_\75;qlQIz^Z uUg~"V~i8\ma{a;#[\*trymA+}_1u78`XIN:~NN+7n#LPd|8CQ=};x,R$n]vrr$OAjuk @,`d*))M8vzE>_a,m#Jh(J"D":dR!JUZ_XszLVzUt2IK;Y mzyvKXE\|j:,zHS] o>Quuu0 DfyJR[FpieH,FsSrWvJt+ %]V EN>Hw )f+`dzdrUe?X(OmRL!##pg~F#n9ZSd! Continue Reading. Download Free PDF. endstream Picture courtesy: Matrix Factorization Techniques for Recommender Systems: Koren et al, 2009 Probabilistic Machine Learning (CS772A) Probabilistic Matrix Factorization 4 . Collaborative filtering is generally more accurate then content filtering however, it suffers from cold start problem. DataJobs.com: Analytics Jobs, Hadoop Jobs, DBA Jobs, Data Science Jobs View 1 excerpt, cites background <>/Font<>/XObject<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 720 540] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> Then make recommendations based on underlying latest factors. These contentboosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily. Table of Contents: Basic Ideas. LXAI: Intro to Recommendation Systems with Matrix FactorizationPart I - Conceptual presentation of recommendation systems with matrix factorization using Net. (2018). Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. The aim of this study was to utilize state-of-the-art recommender system techniques for . 2 0 obj INTRODUCTION, Matrix Factorization and Factorization Machines for cjlin/talks/sdm2015.pdfMatrix Factorization and Factorization Machines for Recommender Systems Chih-Jen Lin Department of Computer, Matrix Factorization Techniques For Recommender Systems, 1.Matrix Factorization Techniques For Recommender As well as incorporate temporal dynamics of the user and the items. The authors propose a matrix factorization approach to missing data imputation that (1) identifies underlying factors to model similarities across respondents and responses and (2) regularizes across factors to reduce their overinfluence for optimal data . View 5 excerpts, references background and methods. 7 0 obj Agenda Recommender System By taking partial derivatives, the update rule would look something like above. A model-based approach for recommendation in social networks, employing matrix factorization techniques and incorporating the mechanism of trust propagation into the model demonstrates that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users. Content delivery networks (CDNs) are key components of high throughput, low latency services on the internet. endobj While this technique works well, until now, there was no general explanation of why it works. Download. 2012 by. This paper incorporates the coupling relationship analysis to capture the under-discovered relationships between items and proposes a neighborhood-based matrix factorization model, which considers both the explicit and implicit correlations between items, to suggest the more reasonable items to user. Explanations, Matrix Factorization (MF), Recommender Sys-tems, Collaborative Filtering (CF) 1. 15 0 obj %PDF-1.5 | Find, read and cite all the research you . sented by a densely filled matrix. <> (2018). x[b\ ( l In this tutorial, we will go through the basic ideas and the mathematics of matrix factorization, and then we will present a simple implementation in Python. endobj Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Matrix factorization is a simple embedding model. Implementation of the winning recommender system from the Netflix competition.Uses matrix decomposition to derive a P and Q matrix which can be used to make predictions. 8 0 obj This paper proposes a novel framework, called tag informed collaborative filtering (TagiCoFi), to seamlessly integrate tagging information into the CF procedure, and demonstrates that TagiCoFi outperforms its counterpart which discards the tagging information even when it is available, and achieves state-of-the-art performance. This paper study different ways to incorporate content information directly into the matrix completion approach of collaborative filtering to achieve better recommendation accuracy. View on IEEE staff.ustc.edu.cn Save to Library Create Alert Please pace yourself carefully -- it will be difficult to finish in two weeks unless you start the assignments during the first week. endobj 3D Photography using Context-aware Layered Depth Inpainting, Applying Price Anomaly Detection in Retail Industry, How to generate recommendations using TF-IDF, Monthly ML: Data Science & Machine Learning Wrap-UpJanuary 2020, The Ultimate and Practical Guide on Feature Scaling, Intellory: Memory management tool using Machine Learning, https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf, https://en.wikipedia.org/wiki/Music_Genome_Project, https://medium.com/@connectwithghosh/simple-matrix-factorization-example-on-the-movielens-dataset-using-pyspark-9b7e3f567536, (2018). Obviously the matrix will be sparse since not everyone is going to watch every movies, (we all have different taste when it comes to movies). <> 11 0 obj Abstract: Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems.To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. <> Then we can create an objective function (that we want to minimize) with respect to q and p, which are (m,k) and (k,n) matrices. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. Reminders Homework8:GraphicalModels -Release:Mon,Apr.17 -Due:Mon,Apr.24at'11:59pm Homework9:'Applicationsof'ML -Release:Mon,Apr.24 -Due:Wed,May3at . The goal of our work is to fill this gap with a twofold contribution. In section 5, we apply those algorithms to a real problem.Finally, Section 6 concludes. This work describes a simple algorithm for incorporating content information directly into matrix factorization approach for collaborative filtering and presents experimental evidence using recipe data to show that this not only improves recommendation accuracy but also provides useful insights about the contents themselves that are otherwise unavailable. <>>> Please note that this post is for my future self to look back and review the materials presented in this paper. Retrieved 22 November 2018, from, Music Genome Project. (In the setting of movie recommendation system). build a genetic-based matrix-factorization RS to make recommendation for the active user. The concept of matrix factorization can be written mathematically to look something like below. 16.12.2014 | Fachbereich Informatik | Recommender Systems Seminar | Patrick Seemann | 18 Online updating MF Models To solve new-user / new-item problem Recalculating whole model infeasible ^R S We have the following scenario: - existing factorization (W,H) and a new user rating comes in ^R S{ru,i} already calculated ratings matrix can only be approximated, because endobj Statistical Analysis and Data Mining: The ASA Data Science Journal, Many businesses are using recommender systems for marketing outreach. <> Yet, in some cases, it is beneficial to use ALS optimization. stream Matrix Factorization Techniques (MF) The most successful way to implement the Latent factor model is Matrix Factorization. Let there be matrix A with dimensionality of (m,n) this matrix can be viewed as a dot product between two matrix with each matrices having dimensions of (m,k) and (k,n). <> <> These days we are constantly being recommended from varieties of sources, such as what blog to read, what music to listen to etc.. And these recommendation systems are becoming more personalized than ever. oa F)'r-%p)_a_(J)a08*bdhSb=*?IJ]uG$~K\bSxKp5Ff} Recommender systems deal with challenging issues such as scalability, noise, and sparsity and thus, matrix and tensor factorization techniques appear as an interesting tool to be exploited.. Using this strength we can estimate if a user is going to like a movie that (he/she) never saw. Published in: Computer ( Volume: 42, Issue: 8, August 2009) endobj 5 0 obj 13 0 obj endobj Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. Abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels .read moreread less It factors the user-item matrix into the product of two lower rank matrices as follows: Where R is the approximated rating matrix of m rows and n columns, U is a user-factor matrix , it has a row for each user (called user factor u), V is a item-factor matrix, it has a column for each item (called item factor v) an integer score from the range of 1 to 5) of items in a recommendation system. 16 0 obj This paper presents an elegant and effective framework for combining content and collaboration, which uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. Given the feedback matrix A R m n, where m is the number of users (or queries) and n is the number of items, the model learns: A user. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. endobj Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent, and the role of transprency (user understanding of why a particular recommendation was made) in Recommender Systems is examined. 1 [E*r_ENouS6"g&v]Zk}4Fgqx$?iD&6{*9?puM+>\i|kj-Lpr=rAQ In this paper we present a novel context-aware recommendation algorithm that extends Matrix Factorization. View 3 excerpts, cites methods and background, 2020 International Conference on Mathematics and Computers in Science and Engineering (MACISE). 18 0 obj Popular matrix factorization algorithms utilized in recommender systems were reviewed. Matrix factorization techniques for context aware recommendation. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. Matrix Factorization Methods Characteristic - Characterizes both items and users by vectors of factors infered from item rating patterns - High correspondence between item and user factors leads to a recommendation Rely on matrix types of input data - One dimention representing user - The other representing items Two data types At this time, if there is a strong relationship between the user and the item, it is recommended. 3 0 obj Retrieved 22 November 2018, from, Simple Matrix Factorization example on the Movielens dataset using Pyspark. When applied to images, such as faces, Lee and Seung (2001) have shown that the learned features are more localized than the ones learned with a classical singular value decomposition. Gediminas Adomavicius, Alexander Tuzhilin, Steffen Rendle, Christoph Freudenthaler +2 more, Copyright @ 2022 | PubGenius Inc. | Suite # 217 691 S Milpitas Blvd Milpitas CA 95035, USA, Matrix Factorization Techniques for Recommender Systems, Machine Learning : A Probabilistic Perspective, Online Learning for Matrix Factorization and Sparse Coding, Exact matrix completion via convex optimization, French Institute for Research in Computer Science and Automation, Using collaborative filtering to weave an information tapestry, Factorization meets the neighborhood: a multifaceted collaborative filtering model, Collaborative Filtering for Implicit Feedback Datasets, Item-based collaborative filtering recommendation algorithms, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, BPR: Bayesian personalized ranking from implicit feedback. Mohamed Abdi. One downside of SVD is the fact that when the original matrix is sparse (incomplete) left and right singular vectors are undefined. G%F666,rM >y@hpdpBst|NbJz``h{ o` n>O\STt'pag5IL"Hl.Htka%i;GM$$-rH222;C77OTIfY4"I7NTSmF;f)"`YZtgt5pt+U8;lG_}%D}3E:s7Z;5Yk-8,v|zK,(x#'pJQQdOum*N Rr]G/a,bYx] {7Y\NS%|Lebgdd"f'K'w# gu2w_\.wbA~41=pM(y{*7HAwd;/p_Fz^QNHNJ$LV u1L[kzv^Dz.mL4hY_l74{'JJrM'GJUGwv({wG`ktt2w}kO) w#*{K~P8S[C?tg)*++kf^U.YTj {D6=08ard&EwRzIN=.Qn.BgnL"_J$I:pZ'/ uL}x'3~RWI]}wqrJZMH_wjrG^^C>VvvLj(|A0Y">Nw Czz3H;|1L\Po?;S{jzSP|>?QP_T*;STWX]*Nrcmn6-\.|RZ>`5-..;_PvHwO%!2{b[{Gvv6h]/YPMN Np8f{}vG_.rZpP(lm\~GQT. In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Continue Reading. First, we analyze the stability of Matrix Factorization, one of the most successful families of embedding-based models for top-N recommendation. The .ipynb_ files include visualizations of RMSE decreasing with iterations when fitting on the training dataset. <> endobj A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Navgaran' et al. SystemsReporter: Lei Guohttp://ir.sdu.edu.cn/index.htmCopyright [ 12 0 R] ermp/xCLKZ-vU[a,8{ ,Hl;d33vRYt+H!)//onmuCo'gygt]}5o3JC&Y ]5qhu?n\#WBjCP-!U323 cX{13:hdKd=t 4 0 obj <> This article reviews the related research and advances in the application of matrix factorization techniques in recommender systems. The proposed weighted AutoEncoding model provides an effective strategy for borrowing strength from user or itemspecific clustering structure as well as pairwise similarity in the training data, while achieving high computational efficiency and dimension reduction, and preserving nonlinear relationships between user preferences and item features. The term on the right is the regularization term, this is added since we do not want our decomposed matrix q and p to over-fit to the original matrix. an integer score from the range of 1 to 5) of items in a recommendation system. stream One strength of matrix factorization is the fact that it can incorporate implicit feedback, information that are not directly given but can be derived by analyzing user behavior. 12 0 obj for implicit feedback [ 13 ], time aspects (different than forgetting) [ 17, 18 ], semi-supervised learning [ 26 ], active learning [ 16 ]. 2011, Proceedings of the fifth ACM conference on Recommender systems - RecSys '11. View 2 excerpts, references background and methods. This makes SC one of the slower popular clustering techniques, and makes it difcult to scale this approach to large data sets. endobj Many computer-based services use recommender systems that predict our preferences based on our degree of satisfaction with the past selections. Where each row represents each users, while each column represents different movies. CDN cache servers have limited storage and bandwidth and implement state-of-the-art cache admission and eviction algorithms to select the most popular and relevant content for the customers served. Collaborative filtering is generally more accurate then content . Download Free PDF. Just like how we added additional bias term to the original function, we can add additional terms to counter the cold start problem. Matrix Factorization Techniques for Recommender Systems; Recommender Systems Jan Boehmer, Yumi Jung, and Rick Wash Michigan State University Email: [email protected] Word Count: 4,744; Recommender System for News Articles Using Supervised Learning; Collaborative Filtering for Binary, Positive-Only Data

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