Journal ArticleDOI
Recommendation information diffusion in social networks considering user influence and semantics
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TLDR
This paper enhances recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended.Abstract:
One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users’ profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items’ general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore, online advertisement efficiency is raised.read more
Citations
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Journal ArticleDOI
Deep learning approach on information diffusion in heterogeneous networks
TL;DR: This work proposes a novel meta-path representation learning approach, Heterogeneous Deep Diffusion (HDD), to exploit meta- Paths as main entities in networks to exploit information diffusion in heterogeneous networks.
Journal ArticleDOI
A fine-grained social network recommender system
Markos Aivazoglou,Antonios O. Roussos,Dionisis Margaris,Costas Vassilakis,Sotiris Ioannidis,Jason Polakis,Dimitris Spiliotopoulos +6 more
TL;DR: A fine-grained recommender system for social ecosystems, designed to recommend media content published by the user’s friends, which developed a proof-of-concept implementation for Facebook and explored the effectiveness of the underlying mechanisms for content analysis.
Journal ArticleDOI
Exploiting Internet of Things information to enhance venues’ recommendation accuracy
TL;DR: This paper introduces a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations and presents a framework which incorporates the above characteristics.
Journal ArticleDOI
Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems
TL;DR: It is established that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and a technique is presented that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social networkuser’s interests, thus improving prediction quality.
References
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Book
Introduction to Information Retrieval
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Journal ArticleDOI
Use of Ranks in One-Criterion Variance Analysis
William Kruskal,W. Allen Wallis +1 more
TL;DR: In this article, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σn i (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ2(C − 1), unless the samples were too small, in which case special approximations or exact tables are provided.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal ArticleDOI
Fab: content-based, collaborative recommendation
Marko Balabanovic,Yoav Shoham +1 more
TL;DR: It is explained how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither, and how the particular design of the Fab architecture brings two additional benefits.
Book ChapterDOI
Collaborative filtering recommender systems
TL;DR: This chapter introduces the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings.