Journal ArticleDOI
Which App? A recommender system of applications in markets
Reads0
Chats0
TLDR
An integrated solution which recommends to the users applications by considering a big amount of information: that is, according to their previously consumed applications, use pattern, tags used to annotate resources and history of ratings is presented.Abstract:
Highlights? We propose a recommender system of applications in Markets. ? It uses five recommendations techniques, more than similar actual recommenders. ? The main input for the recommender is the actual usage of each application made by each user. ? We describe the implemented service for monitoring the users interaction. Users face the information overload problem when downloading applications in markets. This is mainly due to (i) the increasing unmanageable number of applications and (ii) the lack of an accurate and fine-grained categorization of the applications in the markets. To address this issue, we present an integrated solution which recommends to the users applications by considering a big amount of information: that is, according to their previously consumed applications, use pattern, tags used to annotate resources and history of ratings. We focus this paper on the service for monitoring users' interaction.read more
Citations
More filters
Journal ArticleDOI
Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Journal ArticleDOI
Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families
TL;DR: Dendroid, a system based on text mining and information retrieval techniques for malware analysis, is introduced, suggesting that the approach is remarkably accurate and deals efficiently with large databases of malware instances.
Journal ArticleDOI
Hybrid recommendation approaches for multi-criteria collaborative filtering
TL;DR: New recommendation methods using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Self-Organizing Map (SOM) clustering are proposed to improve predictive accuracy of criteria CF and experimental results demonstrate that the proposed hybrid methods remarkably improve the accuracy of multi-criteria CF in relation to the previous methods based on multi-Criteria ratings.
Journal ArticleDOI
A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users
TL;DR: Views on social network data based recommender systems are expressed by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques.
Proceedings ArticleDOI
App recommendation: a contest between satisfaction and temptation
TL;DR: This work proposes an Actual- Tempting model that captures factors that invoke a user to replace an old app with a new app and shows that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation.
References
More filters
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Proceedings ArticleDOI
GroupLens: an open architecture for collaborative filtering of netnews
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.