Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System

Gong, Shimin; Ye, HongWu; Tan, Huobin

doi:10.1109/paccs.2009.66

Cited by 44 publications

(20 citation statements)

References 15 publications

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“…PCC calculates the value between -1 and 1 whereas Cosine Similarity calculates the value between 0 and 1. [7]. It is widely used in few ecommerce sites like Amazon .…”

Section: Memory-based Approach-mentioning

confidence: 99%

Suggestive Approaches to Create a Recommender System for GitHub

Sharma¹,

Mahajan²

2017

IJITCS

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Abstract-Recommender system suggests users with options that may be of use to them or may be of their interest or liking. These days recommender systems are used widely on most systems and especially on those which are connected to World Wide Web, it may be a mobile app, a desktop application, or a website. Most advertisements on these systems are focused on targeting a specific group. Recommender systems provide a solution to such a scenario where the recommendations need to be targeted based on a user profile. Almost all commercial, collaborative or even social networking websites rely on recommender systems. In this paper, we specifically focus on GitHub, a source code hosting site and one of the most popular platforms for online collaborative coding and sharing. GitHub offers an opportunity for researchers to perform analysis by providing REST-based APIs for downloading its data. GitHub hosts a vast amount of user repositories so it is quite difficult for a GitHub user to decide to which repository she should contribute on GitHub. So, our paper aims to review different approaches that can be used for creating a recommender system for GitHub, to provide personalized suggestions to GitHub users to which repositories they should contribute. In this paper, we have discussed collaborative filtering, content-based filtering, and hybrid filtering, knowledge-based and utility-based approaches of a recommender system. IndexTerms-Recommender Systems, GitHub, Collaborative filtering, Content-based filtering, hybrid filtering, Knowledge-based approach, Utility-based approach.

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“…PCC calculates the value between -1 and 1 whereas Cosine Similarity calculates the value between 0 and 1. [7]. It is widely used in few ecommerce sites like Amazon .…”

Section: Memory-based Approach-mentioning

confidence: 99%

Suggestive Approaches to Create a Recommender System for GitHub

Sharma¹,

Mahajan²

2017

IJITCS

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“…Collaborative filtering systems are classified based on the nature of their algorithmic technique into memory-based and model-based approaches [3]. Model-based techniques use previous user activities to first learn a predictive model (typically using some statistical or machine-learning methods), which is then used to make recommendations.…”

Section: Collaborative Filtering Approach and Algorithm Definitionsmentioning

confidence: 99%

“…If the dataset has not a normalized distribution, Spearman can give better results than Pearson correlation. The Spearman correlation is calculated as in (3).…”

Section: B Algorithm Definitions 1) Prediction Algorithmmentioning

confidence: 99%

Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems

Tapucu

Kasap

Tekbacak

2012

2012 IEEE 36th Annual Computer Software and Applications Conference Workshops

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Abstract-Recommender systems have a goal to make personalized recommendations by using filtering algorithms. Collaborative filtering (CF) is one of the most popular techniques for recommender systems. As usual, huge number of the datasets on the Internet increase the amount of time to work on data. This challenge enforces people to improve better algorithms for processing data with user preferences and recommending the most appropriate item to the users. In this paper, we analyze CF algorithms and present results for combined user-based/item-based CF algorithms for different size of datasets. Our goal is to show combined solution results using Loglikelihood, Spearman, Tanimoto and Pearson algorithms. The contribution is to describe which user based CF algorithms and user/item based combined CF algorithms perform better according to dataset, sparsity, execution time and k-neighborhood values.

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“…[2,3,4,5,7]. Traditional CF filters useless information by calculating item-item similarities or user-user similarities, and the direct factor in similarity calculating is user-item rating.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Filtering Algorithm Based on User Activity Level

Yongli

Song

et al. 2012

2012 Fifth International Conference on Business Intelligence and Financial Engineering

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Collaborative Filtering Algorithm is one of the most successful recommender technologies, and has been widely used in E-commerce. However, traditional Collaborative Filtering often focus on user-item ratings, but ignore the information implicated in user activity which means how and how often a user makes operations in a system, so it misses some important information to improve the prediction quality. To solve this problem, we bring user activity factor into collaborative filtering and propose a new collaborative filtering algorithm based on user activity level (UACF) . Finally, experiments have shown that our new algorithm UACF improves the precision of traditional collaborative filtering.

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Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System

Cited by 44 publications

References 15 publications

Suggestive Approaches to Create a Recommender System for GitHub

Suggestive Approaches to Create a Recommender System for GitHub

Performance Comparison of Combined Collaborative Filtering Algorithms for Recommender Systems

A Collaborative Filtering Algorithm Based on User Activity Level

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