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Filters
TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings
2017
International Journal of Advanced Research in Computer Science and Software Engineering
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Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. ...
implicit influence of trusted users on the prediction of items for an active user. ...
Matrix Factorization Using User Trust Information: User trust applied to social collaborative filtering techniques in [8] show how trust based social collaborative filtering techniques work well in case ...
doi:10.23956/ijarcsse.v7i11.422
fatcat:gwqftsrkozbg5a3rurtpthg2bi
Trust based recommender system using ant colony for trust computation
2012
Expert systems with applications
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Collaborative Filtering (CF) technique has proven to be promising for implementing large scale recommender systems but its success depends mainly on locating similar neighbors. ...
Jester dataset and MovieLens dataset (available online) and compared with traditional Collaborative Filtering based approach for generating recommendations. ...
Two computational models of trust namely profile-level trust and item level trust have been developed and incorporated into standard Collaborative Filtering frameworks (O'Donovan and Smith, 2005) . ...
doi:10.1016/j.eswa.2011.07.124
fatcat:fjl5k3yeijgovgpv3kuxmol65m
A Review on User Recommendation System Based Upon Semantic Analysis
2017
International Journal of Advanced Research in Computer Science and Software Engineering
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In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative ...
filtering to improve the coverage of recommendation. ...
Expertise, trust and reputation models are incorporated in collaborative RS to increase their accuracy and reliability. ...
doi:10.23956/ijarcsse.v7i11.465
fatcat:o3hz2q3x7vdudbxnisqk5xsx44
Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
2018
Computer and Information Science
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A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. ...
The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. ...
, Ludwig, & Ricci, 2011) and Contextual SLIM . ...
doi:10.5539/cis.v11n2p1
fatcat:vyyrbt7exba2bhufdwoyrad3fa
Review of Social Collaborative Filtering Recommender System's Methods
2016
International Journal Of Engineering And Computer Science
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Recommender systems are now using the social information for their analysis and prediction process. ...
Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user. ...
Collaborative Filtering Collaborative Filtering technique is the most accepted techniques of the RS. ...
doi:10.18535/ijecs/v4i11.06
fatcat:4id6npgcvfdhfo4i7if4y7k56i
Review of Social Collaborative Filtering Recommender System's Methods
2016
International Journal Of Engineering And Computer Science
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Recommender systems are now using the social information for their analysis and prediction process. ...
Social Networking Sites provide users a platform to connect and share their information with other users who share similar interests with user. ...
Collaborative Filtering Collaborative Filtering technique is the most accepted techniques of the RS. ...
doi:10.18535/ijecs/v4i10.49
fatcat:roo2xsud3fhmrhca7wjeawcy7y
Combining trust in collaborative filtering to mitigate data sparsity and cold-start problems
2014
2014 4th International Conference on Computer and Knowledge Engineering (ICCKE)
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for this similar neighbor by using the ratings of her directly trusted neighbors and applying MoleTrust algorithm, so as to incorporate more similar users to generate prediction for this target item. ...
Collaborative filtering (CF) is the most popular approach to build recommender systems and has been successfully employed in many applications. ...
In conclusion, we proposed a new way to better apply trust, similarity and significance to improve the performance of collaborative filtering. ...
doi:10.1109/iccke.2014.6993351
fatcat:sxpfhadufzdkrinpzdbkgroevm
Improving Recommender Systems by Incorporating Similarity, Trust and Reputation
2014
Journal of Internet Services and Information Security
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In this study, we propose a method that can improve the recommender systems by combining similarity, trust and reputation. ...
Throughout our 2 different scenarios of experiment simulations conducted on MovieLens dataset and the comparison of our results with other trust-based collaborative filtering research, we found out that ...
Collaborative filtering systems [20] identify similar users and analyze their preferences to generate recommendation. ...
doi:10.22667/jisis.2014.02.31.064
dblp:journals/jisis/ThanH14
fatcat:33wv5rt6dbdjzayydh2vhu25je
A Computational Model for Trust-Based Collaborative Filtering
[chapter]
2014
Lecture Notes in Computer Science
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To alleviate this problem, trust has been incorporated in collaborative filtering (CF) approaches with encouraging experimental results. ...
In this paper, we propose a computational model for trust-based CF combined with k-means clustering, k-nearest neighbor (kNN) and three different methods to infer trust, based on a detailed data analysis ...
Section 2 surveys existing research work on recommender systems, collaborative filtering and the rising interest to incorporate trust into CF. ...
doi:10.1007/978-3-642-54370-8_22
fatcat:givomxcmz5hu7fd43wgdxz3ap4
Similarity-based Techniques for Trust Management
[chapter]
2010
Web Intelligence and Intelligent Agents
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In (Hwang & Chen, 2007) an improved mechanism to the standard collaborative filtering techniques by incorporating trust into collaborative filtering recommendation process is presented. ...
users, item-based similarity instead of user-based similarity, and content-boosted collaborative filtering (see (Papagelis et al., 2005) ). ...
Similarity-based Techniques for Trust Management, Web Intelligence and Intelligent Agents, Zeeshan-Ul-Hassan Usmani (Ed.), ISBN: 978-953-7619-85-5, InTech, Available from: http://www.intechopen.com/books ...
doi:10.5772/8386
fatcat:aqgkxzkwqfa33jwtncjyr2gaoq
Trust in recommender systems
2005
Proceedings of the 10th international conference on Intelligent user interfaces - IUI '05
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We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. ...
And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recommendations by leveraging the preferences ...
In this section we define two models of trust and show how they can be readily incorporated into the mechanics of a standard collaborative filtering recommender system. ...
doi:10.1145/1040830.1040870
dblp:conf/iui/ODonovanS05
fatcat:etxkucqx5zdytpfb2ok5v6gmgy
Survey on Users Ranking Pattern based Trust Model Regularization in Product Recommendation
2018
International Journal of Trend in Scientific Research and Development
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The proposed system is a new framework for social trust data from four real-world datasets, which indicates that not only the explicit and implicit impact of ratings and trust should be considered in the ...
Trust SVD extends to SVD ++, using the explicit and implicit impact of rated projects by further combining the explicit and implicit impact of trust and trust users on active user project predictions. ...
The basic assumption of collaborative filtering is that active users will like items that similar users like. ...
doi:10.31142/ijtsrd11302
fatcat:7rkgttt4lndgze4ygnegom3ls4
Social Network and Device Aware Personalized Content Recommendation
2014
Procedia Technology - Elsevier
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We present an approach that considers user experienced items, users direct trust and the capability of contributing to the content network for finding similarity. ...
The proposed recommender system presents some novelties that are not provided in existing collaborative and content-based filtering. ...
Improve the users' similarity by incorporating the Pearson Correlation Coefficient, Evaluated resources and direct trust in the form of opinions and comments. ...
doi:10.1016/j.protcy.2014.10.260
fatcat:mqkcjghuhrhshdq2rgfpx3s2cm
Analyzing Correlation between Trust and User Similarity in Online Communities
[chapter]
2004
Lecture Notes in Computer Science
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However, in order to provide meaningful results for recommender system applications, we expect notions of trust to clearly reflect user similarity. ...
Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. ...
Our motivation mainly derives from incorporating trust models into decentralized recommender systems, exploiting trust not only for selecting small neighborhoods upon which to perform collaborative filtering ...
doi:10.1007/978-3-540-24747-0_19
fatcat:slz7tb4ld5bfjftnagxmj6rkjq
Trust Based Novel Recommendation Regularized with Item Ratings
2017
International Journal for Research in Applied Science and Engineering Technology
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In order to enhance the novel recommendation model, we propose a trust based recommendation model with item rating where data sparsity and cold start problem are rectified.We make use of personalized social ...
and specification. ...
Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. ...
doi:10.22214/ijraset.2017.4086
fatcat:lzwdh4eimzaa3junt2yng6i7lu
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