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Cleaned Similarity for Better Memory-Based Recommenders [article]

Farhan Khawar, Nevin L. Zhang
2019 pre-print
We quantify this overestimation and present a simple re-scaling and noise cleaning scheme. This results in better performance of the memory-based methods compared to their vanilla counterparts.  ...  Memory-based collaborative filtering methods like user or item k-nearest neighbors (kNN) are a simple yet effective solution to the recommendation problem.  ...  Broadly speaking, CF methods are generally characterized into memory-based and model-based methods. Memory-based methods are known for their simplicity and competitive performance [6] .  ... 
doi:10.1145/3331184.3331310 arXiv:1905.07370v1 fatcat:lodbv6dizrda7k6g7nlkeq6jda

Text Based Restaurant Recommendation System using End-To-End Memory Network

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
We suggest using a novel memory based end-to-end network mechanism to reduce the need for long term dependencies and to reduce the need for memory intensive systems.  ...  This paper tries to focus on improving the text based recommendation systems that can be implemented to leverage the vast review data that can be found on websites.  ...  In (Huang, 2016), the hashtag recommendation system based on end to end memory, a model similar to recommendation system for restaurants is suggested.  ... 
doi:10.35940/ijitee.b8108.019320 fatcat:orvk3yvejjfvjed7iywg2ncv7y

Word from the editors

Constantin Orăsan, Marcello Federico
2016 Machine Translation  
The paper Self-selection Bias of Similarity Metrics in Translation Memory Evaluation by Wolff et al. explores the impact of using different similarity metrics for evaluating TMs and how TM evaluation metrics  ...  The paper Combining Off-the-shelf Components to clean a Translation Memory by Wolff describes a system that participated in the Automatic Translation Memory Cleaning shared task.  ... 
doi:10.1007/s10590-017-9192-4 fatcat:m4fdlqlcmvgqbojzpes5h5tsei

Hy-MOM: Hybrid Recommender System Framework Using Memory-Based and Model-Based Collaborative Filtering Framework

Gina George, Anisha M. Lal
2022 Cybernetics and Information Technologies  
Firstly, it attempts to find the neighbourhood of similar learners based on certain learner characteristics by applying a user-based collaborative filtering approach.  ...  The outcome of the first stage is merged with the second stage to generate recommendations for the learner.  ...  This is then sent for pre-processing. The cleaned matrix is given as input for the User-based Collaborative filtering. A set of similar learners based on learner's characteristics is generated.  ... 
doi:10.2478/cait-2022-0009 fatcat:izvdxzbcmrci7agl3bdrymsvii

BOOK RECOMMENDATION SYSTEM JUST READ IT!

Mr. D JAYARAM, Dr. G. N. R. PRASAD, ISHIKA GUPTA
2022 IJARCCE  
Recommendation System (RS) is software that suggests similar items to a purchaser based on their earlier purchases or preferences.  ...  The top ten books will be recommended to the user that is the most similar to the book that they have entered.  ...  Similar tasks were performed for data cleaning as the first dataset.  ... 
doi:10.17148/ijarcce.2022.11602 fatcat:igw6ouux5nfdrnkdoq34isjuqy

Product Recommendation in Offline Retail Industry by using Collaborative Filtering

Bayu Yudha Pratama, Indra Budi, Arlisa Yuliawati
2020 International Journal of Advanced Computer Science and Applications  
Another finding is that the more data training being used, the better the performance of the recommendation system will result.  ...  For an offline retailer, promoting specific products based on the markets' taste is quite challenging because of the unavailability of information regarding customers' preferences.  ...  This approach is better than the Memory-based approach in several ways [19] .  ... 
doi:10.14569/ijacsa.2020.0110975 fatcat:v4g7n2u52be4ncnzqlhaytowae

Self-Guided Learning to Denoise for Robust Recommendation [article]

Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng
2022 arXiv   pre-print
Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent.  ...  Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to various  ...  The memory rate is the proportion of memorized data (see Section 3.1.1 for details) in the clean and noisy interactions.  ... 
arXiv:2204.06832v1 fatcat:tajlofkk4vazjevt7jelwo2a7u

Data exploration

Anna Gogolou, Marialena Kyriakidi, Yannis Ioannidis
2016 Proceedings of the Third International Workshop on Exploratory Search in Databases and the Web - ExploreDB '16  
In this paper, we claim that "any" userdata interaction is essential for data exploration and sketch a prototype with both automated and user-induced functionality.  ...  Or it may recommend a more specialized search than those already posed by the user, based on how other similar users proceeded with their . exploration and eventually succeeded, having followed similar  ...  DCV DCV is a web-based tool that has been initially designed and developed exclusively for data cleaning, able to automatically detect inconsistencies and outliers in tabular datasets (Fig. 2) .  ... 
doi:10.1145/2948674.2955105 dblp:conf/sigmod/GogolouKI16 fatcat:nxx7fwngxvctflw6c7dtgabkha

Neural Architectures for Correlated Noise Removal in Image Processing

Cătălina Cocianu, Alexandru Stan
2016 Mathematical Problems in Engineering  
The performance of the proposed method is evaluated by a long series of tests, the results being very encouraging as compared to similar developments for noise removal purposes.  ...  The paper proposes a new method that combines the decorrelation and shrinkage techniques to neural network-based approaches for noise removal purposes.  ...  means and their compressed versions some amount of noise is expected to be removed, for each value of the index ; that is, the compressed versions of the means are expected to be better cleaned variants  ... 
doi:10.1155/2016/6153749 fatcat:5r74a5kz3bg7tbkhnwh6kwfe4e

Research of Distinct Algorithm of Short Text Based on Simhash

Yun ZHANG, Zong-ze JIN, Wei-min MU, Wei-ping WANG
2017 DEStech Transactions on Engineering and Technology Research  
The efficiency is also improved for microblog de-duplication.  ...  Through experiments we can gain the results of B-Simhash, which is better in processing the short text. Adding the special character processing, the result is better than the original.  ...  BloomFilter is based on memory, so it needs a better processing capacity. However, the defects of the algorithm are also obvious.  ... 
doi:10.12783/dtetr/oect2017/16127 fatcat:dimwmk3yufcgfbtgtlmpjujb3u

Game Teaching Method in Preschool Education Based on Big Data Technology

Rui Bai, Le Sun
2021 Scientific Programming  
Based on the collaborative filtering algorithm of preschool children, this paper estimates the current preschool children's score for the game by referring to the scores of neighbor preschool children  ...  system is mainly guaranteed through lowquality label cleaning and high-quality label recommendation. e entire cleaning process is shown in Figure 2 . e nonreferenced tags are primarily screened by identifying  ...  It shows that adjusting the cosine similarity is more suitable for use in game-based methods than the Pearson correlation coefficient.  ... 
doi:10.1155/2021/4751263 fatcat:r6non24z5bcelc2sxb2cy6d4fq

Recommendation System for Web Mining: A Review

Purvi Dubey, Pramod S. Nair
2015 International Journal of Computer Applications  
In this paper we review some of the recommendation systems. We discuss by looking at its merits and demerits accordance to the applications.  ...  system and memory based system, model based system and temporal information used for recommendation process to recommend faster.  ...  It used hybrid structure system and model based system and memory based system, model based system and temporal information used for recommendation process to recommend faster.  ... 
doi:10.5120/19229-0941 fatcat:2bqtuxyouncylbkypnsprdzqyq

Movies Recommendation System using Cosine Similarity

Shubham Pawar, Pritesh Patne, Priya Ratanghayra, Simran Dadhich, Shree Jaswal
2022 Zenodo  
These results are based on similar traits/demographics of the movie that has been searched. Content based filtering is a technique that is used to recommend movies.  ...  Hence the main focus of our recommendation system is to provide a total of ten movie recommendations to users who searched for a movie that they like.  ...  Using the Pearson Correlation Coefficient Based recommended system the similarity between users can be easily determined, but it is a long formula-based method that requires a lot of computation and memory  ... 
doi:10.5281/zenodo.6503164 fatcat:whvbn6c4erclzjw6gddzezl6l4

Given Users Recommendations Based on Reviews on Yelp [article]

Shuwei Zhang, Maiqi Tang, Qingyang Zhang, Yucan Luo, Yuhui Zou
2021 arXiv   pre-print
In the end, with the help of similarity scores, we are able to recommend users the most matched restaurant based on their recorded reviews.  ...  For our hybrid recommendation system, we have two major components: the first part is to embed the reviews with the Bert model and word2vec model; the second part is the implementation of an item-based  ...  At the end, with the help of similarity scores, we are able to recommend users the most matched restaurant based on their recorded reviews.  ... 
arXiv:2112.01762v1 fatcat:gtx5ycfcazaqpibof7wgkzdbke

Implementing Recommender Systems using Machine Learning and Knowledge Discovery Tools

Mohammad Zahrawi, Ahmad Mohammad
2021 Knowledge-Based Engineering and Sciences  
This article discovers the different characteristics and features of many approaches used for recommendation systems in order to filter and prioritize the relevant information and work as a compass for  ...  As a proof of the importance of recommender engine, it can be stated that Netflix arrange a challenge (the "Netflix prize") where the mission was to create a recommender engine that achieves better than  ...  Table 1 reports the samples of each datasets for better understand. These datasets are clean, organized, free of missing values.  ... 
doi:10.51526/kbes.2021.2.2.44-53 fatcat:ludyzq53d5gubl3xmkvj2zku64
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