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Developing an intelligent trip recommender system by data mining methods
2016
Global Journal of Information Technology Emerging Technologies
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Recommender systems are being used in almost every search related area. Tourism domain is one of these sectors. ...
Proposed approach predicts clusters for system users and according to these user clusters, trips, hotels and such services can be recommended individually or as a campaign to target user or user groups ...
A recommender system tries to predict a rating value of an item for a target user. To perform an accurate prediction, such systems use their member profiles and member behaviors on the system [1] . ...
doi:10.18844/gjit.v6i1.398
fatcat:ydw3qhwnq5exffntsmdntxqn4e
A Survey of Collaborative Filtering Techniques
2009
Advances in Artificial Intelligence
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As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown ...
preferences for other users. ...
Miroslav Kubat and Moiez A. Tapia for their help during the early stage of this paper and also to Drs. Xingquan Zhu, Russ Greiner, Andres Folleco, and Amri Napolitano for their comments. ...
doi:10.1155/2009/421425
fatcat:qtbk7gfqtvhzhg6dqqrf2drjzy
Automatic Preference Mining through Learning User Profile with Extracted Information
[chapter]
2004
Lecture Notes in Computer Science
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The proposed method was tested in database that users estimated the preference about web pages, and certified that was more efficient than existent methods. ...
Apriori algorithm extracts characteristic of web pages in form of association words that reflects semantic relation and it mines association words from learning the ontological user profile. ...
Introduction Recommender systems using information filtering accumulates a database of users preferences, and then uses them to make personalized recommendations for items such as books, music, clothing ...
doi:10.1007/978-3-540-27868-9_89
fatcat:uqp7zdeq35a47hw6ovyp4kvray
Hierarchical Latent Factors for Preference Data
2012
Sistemi Evoluti per Basi di Dati
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In this work we propose a probabilistic hierarchical generative approach for users' preference data, which is designed to overcome the limitation of current methodologies in Recommender Systems and thus ...
to meet both prediction and recommendation accuracy. ...
Conclusion In this work we proposed a hierarchical Bayesian approach for preference data, which extends state-of-the-art (hierarchical) co-clustering techniques, by modeling dynamic associations and dependencies ...
dblp:conf/sebd/BarbieriMR12
fatcat:qzhh64q5zbae7jz772dufwnadm
A Bayesian Inference based Hybrid Recommender System
2020
IEEE Access
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In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. ...
It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of model-based methods. ...
To alleviate the item targeting problem, in this paper, we propose a hybrid recommender system that uses Bayesian estimation to predict users' interests. ...
doi:10.1109/access.2020.2998824
fatcat:5dvcqvp265aenj6fkqoluopzqa
Learning multiple models for exploiting predictive heterogeneity in recommender systems
2011
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems - HetRec '11
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Such predictive heterogeneity is likely to occur in large recommender systems that involve a diverse set of users, and can be mitigated by using multiple localized predictive models rather than a single ...
The proposed approach can incorporate different types of inputs to predict the preferences of diverse users and items. ...
The use of user and item vectors whose elements correspond exactly to one another is common in content-based recommendation systems [6] . ...
doi:10.1145/2039320.2039323
fatcat:3fnfa4qxxrfzdo4ysore3rb4me
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
[article]
2013
arXiv
pre-print
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Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. ...
This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. ...
Max Chickering, David Hovel, and Robert Rounthwaite contributed to the programming of the algorithms that were used in this study. ...
arXiv:1301.7363v1
fatcat:23wnnzaal5b37b7xofpqf67vgy
Integration of Bayesian Theory and Association Rule Mining in Predicting User's Browsing Activities – Survey Paper
2015
International Journal of Computer Applications Technology and Research
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Association rule mining method has been widely used in recommender systems in profiling and generating users' preferences. ...
These vices could be managed using recommender systems methods which are used to deliver users' preference data based on their previous interests and in relation with the community around the user. ...
Due to internet development and the increase in the number of users, recommender systems for web applications were introduced to anticipate users' preferences in terms of content and information based ...
doi:10.7753/ijcatr0410.1005
fatcat:gnicnzqyjfhzzcejvowtsnob6i
You are what you consume
2013
Proceedings of the 7th ACM conference on Recommender systems - RecSys '13
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In our approach, we model both user preferences and items under recommendation as multivariate Gaussian distributions; and make use of Normal-Inverse Wishart priors to model the recommendation agent beliefs ...
We then interpret these ratings in an innovative way, using them to guide a Bayesian updating process that helps us both capture a user's current mood, and maintain her overall user type. ...
Research in recommendation systems attempts to understand a user's needs, preferences and mood, and help her make a decision. ...
doi:10.1145/2507157.2507158
dblp:conf/recsys/BabasCT13
fatcat:vujz46f5qvcwdo3rfwkp3gwika
Recommendation systems: Principles, methods and evaluation
2015
Egyptian Informatics Journal
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This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation ...
Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. ...
Once clusters have been formed, the opinions of other users in a cluster can be averaged and used to make recommendations for individual users. ...
doi:10.1016/j.eij.2015.06.005
fatcat:arp4euyhifhvppxf6z46rcuyqu
Personalized and Situation-Aware Multimodal Route Recommendations: The FAVOUR Algorithm
2017
IEEE transactions on intelligent transportation systems (Print)
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2016 pre-print arXiv:1602.09076v1 fatcat:dg5obj3u4ngkxlqt2yrywevnay
In particular the definition of the mass preference prior for initialization of step two is shown to provide better predictions than a number of alternatives from the literature. ...
In this step a mass preference prior is used to encode the prior knowledge on preferences from the class identified in step one. ...
The projects were funded through the Climate and Energy Funds (KLIEN) of the Austrian Ministry for Transport, Innovation and Technology (BMVIT). ...
doi:10.1109/tits.2016.2565643
fatcat:fuc2zx76jbaqbkc5tmroydxfcu
Recommendation Systems for E-Commerce: A Review
2017
IJARCCE
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In future,enhanced clustering algorithms as well as better prediction generation schemes which is used to improve prediction quality for e-commerce have to developed. ...
Recommendation system is an intelligent system that generates a ranked list of items on which a user might be interested. ...
For the estimation of rating, similarities between items and users are predicted using different approaches. ...
doi:10.17148/ijarcce.2017.6496
fatcat:657sncidxrcezfoczizqpdj5ye
Suggestive Approaches to Create a Recommender System for GitHub
2017
International Journal of Information Technology and Computer Science
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Recommender system suggests users with options that may be of use to them or may be of their interest or liking. ...
In this paper, we have discussed collaborative filtering, content-based filtering, and hybrid filtering, knowledge-based and utility-based approaches of a recommender system. ...
Based on the values of support and confidence recommendations are provided to users [12] .Association Rule Mining is fast to implement and deals well with large sets of data [14] . ...
doi:10.5815/ijitcs.2017.08.06
fatcat:utye6lgs65frfnrauhyr2fdbxq
Experimental Evaluation of Three Value Recommendation Methods in Interactive Configuration
2020
Journal of universal computer science (Online)
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The second one, that we propose here, is to learn a model from the entire sample as representation of the users' preferences, and to use it to recommend a pertinent value; three families of models are ...
The present work deals with the recommendation of values in interactive configuration, with no prior knowledge about the user, but given a list of products previously configured and bought by other users ...
used on-line to recommend a pertinent value; three families of models are experimented here: Bayesian networks, naive Bayesian networks and lexicographic preferences trees. ...
doi:10.3897/jucs.2020.018
fatcat:o37h54hmk5grvktkq6hbh2eihm
A Model for a Collaborative Recommender System for Multimedia Learning Material
[chapter]
2004
Lecture Notes in Computer Science
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In a cluster of many servers containing heterogeneous multimedia learning material and serving users with different backgrounds (e.g. language, interests, previous knowledge, hardware and connectivity) ...
This is the case of the COLDEX project. Recommender systems have been used to help people sift through all the available information to find that most valuable to them. ...
For calculating the estimated increase to this value a certain document Doc jx may cause by downloading and using it our system uses the vector TLC xi and the estimated evaluation of the learning contribution ...
doi:10.1007/978-3-540-30112-7_24
fatcat:xcythrfuk5apnmmsylbv2isvbi
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