Social-Based Collaborative Filtering

Stefanidis, Kostas; Ntoutsi, Eirini; Kondylakis, Haridimos; Velegrakis, Yannis

doi:10.1007/978-1-4614-7163-9_110171-1

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…In general, a recommender system aims at providing suggestions to users or groups of users by estimating their item preferences and recommending those items featuring the maximal predicted preference. Typically, depending on the type of the input data, i.e., user behavior, contextual information, item/user similarity, recommendation approaches are classified as content-based [19], collaborative filtering [20], knowledge-based [4], hybrid [2], or even social ones [22]. Nowadays, recommendations have more broad applications, beyond products, like links (friends) recommendations [28], query recommendations [6], healthrelated recommendations [23,24], open source software recommendations [11], diverse venue recommendations [7], recommendations for groups [14,16,17], sequential recommendations [25,3] or even recommendations for evolution measures [21,27].…”

Section: Other Work On Recommender Systemsmentioning

confidence: 99%

A Hybrid Recommender System for Steam Games

Gong

Stefanidis

2020

Communications in Computer and Information Science

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A recommender system can be considered as an information filtering system that seeks to predict the preference a user would have for a data item. It is commonly utilized in digital stores to recommend products to their users according to the users previous purchases. This applies to Steam as well, a widely used digital distribution platform for games. The existing recommender system mainly suggests new games to a given user by calculating similarities between games they own and those that they do not. These similarities are based on predefined attributes (game genres). Additionally, the system is able to recommend games based on the game preferences of the users friends. In this work, we target at creating an enhanced recommender system for Steam. The goal is to design a hybrid approach for producing suggestions that will utilize data, such as playing time, game price and game release date, in addition to the genres and the preferences of friends.

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Section: Other Work On Recommender Systemsmentioning

confidence: 99%

A Hybrid Recommender System for Steam Games

Gong

Stefanidis

2020

Communications in Computer and Information Science

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TwiFly: A Data Analysis Framework for Twitter

et al. 2020

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Over the last decade, there have been many changes in the field of political analysis at a global level. Through social networking platforms, millions of people have the opportunity to express their opinion and capture their thoughts at any time, leaving their digital footprint. As such, massive datasets are now available, which can be used by analysts to gain useful insights on the current political climate and identify political tendencies. In this paper, we present TwiFly, a framework built for analyzing Twitter data. TwiFly accepts a number of accounts to be monitored for a specific time-frame and visualizes in real time useful extracted information. As a proof of concept, we present the application of our platform to the most recent elections of Greece, gaining useful insights on the election results.

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Social-Based Collaborative Filtering

Cited by 2 publications

References 27 publications

A Hybrid Recommender System for Steam Games

A Hybrid Recommender System for Steam Games

TwiFly: A Data Analysis Framework for Twitter

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