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Diversification and refinement in collaborative filtering recommender

Rubi Boim, Tova Milo, Slava Novgorodov
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
This paper considers a popular class of recommender systems that are based on Collaborative Filtering (CF) and proposes a novel technique for diversifying the recommendations that they give to users.  ...  We illustrate the operation of DiRec in the context of a movie recommendation system and present a thorough experimental study that demonstrates the effectiveness of our recommendation diversification  ...  PRIORITY-MEDOIDS We start by providing the needed background and notation for Collaborative Filtering (CF).  ... 
doi:10.1145/2063576.2063684 dblp:conf/cikm/BoimMN11 fatcat:bde5oup455brvd5u2jqmi7ntmu


Kunika Arora
2017 International Journal of Advanced Research in Computer Science  
For both users and service providers, recommendation system is profitable and it is also effective in increasing sales of many products.  ...  So, recommendation system is used to resolve this problem. Recommendation system filters out the information fragment according to user behaviour or interest.  ...  . • Cascade hybridization: In this, the recommendation of one technique is refined by another recommendation technique.  ... 
doi:10.26483/ijarcs.v8i7.4308 fatcat:674hj42fjjatrdv4xg2cc2fkba

Improving Reachability and Navigability in Recommender Systems [article]

Daniel Lamprecht, Markus Strohmaier, Denis Helic
2015 arXiv   pre-print
In this paper, we investigate recommender systems from a network perspective and investigate recommendation networks, where nodes are items (e.g., movies) and edges are constructed from top-N recommendations  ...  (ii) What is the influence of parameters, in particular different recommendation algorithms and the number of recommendations shown, on reachability and navigability?  ...  Success ratios were better for a larger number of recommendations and were also improved by diversification. Collaborative Filtering (CF) led to better results than Content-Based methods (CB).  ... 
arXiv:1507.08120v1 fatcat:wjvolmco5vcylfg4fems6445wm

A Single-Step Approach to Recommendation Diversification

Sang-Chul Lee, Sang-Wook Kim, Sunju Park, Dong-Kyu Chae
2017 Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion  
This paper addresses recommendation diversification. Existing diversification methods have a difficulty in dealing with the accuracy-diversity tradeoff.  ...  We propose a novel method to simultaneously optimize the user preference and diversity of k -items to be recommended.  ...  We compared our method with two existing diversification methods, denoted as TD (topic diversification) [3] and DRCF (diversification and refinement in collaborative filtering) [1] .  ... 
doi:10.1145/3041021.3054220 dblp:conf/www/LeeKPC17 fatcat:u2zuxlqbvrbornwlwsjlll6eei

Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs

Zi-Ke Zhang, Tao Zhou, Yi-Cheng Zhang
2010 Physica A: Statistical Mechanics and its Applications  
Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations.  ...  Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles  ...  [30] proposed a diffusion-based top-k collaborative filtering, which performs better than the pure top-k collaborative filtering and pure diffusion-based algorithm.  ... 
doi:10.1016/j.physa.2009.08.036 fatcat:dgsadgjcrrdehfmhyxrvbselbm

It takes variety to make a world

Cong Yu, Laks Lakshmanan, Sihem Amer-Yahia
2009 Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology - EDBT '09  
Recommendations in collaborative tagging sites such as and Yahoo!  ...  We demonstrate their efficiency and effectiveness in diversification on two real life data sets: and Yahoo! Movies.  ...  In collaborative filtering recommendation, one highly indicative factor of those costs is the number of people in the user's network.  ... 
doi:10.1145/1516360.1516404 dblp:conf/edbt/YuLA09 fatcat:mw53uqqx5nadpiun5wzmorcvxy

Taxonomy-driven computation of product recommendations

Cai-Nicolas Ziegler, Georg Lausen, Lars Schmidt-Thieme
2004 Proceedings of the Thirteenth ACM conference on Information and knowledge management - CIKM '04  
Recommender systems have been subject to an enormous rise in popularity and research interest over the last ten years.  ...  We exploit such taxonomic background knowledge for the computation of personalized recommendations.  ...  Remember that collaborative filtering does not consider the content of products but only ratings assigned. Hence, diversification and collaborative filtering intrinsically exclude each other.  ... 
doi:10.1145/1031171.1031252 dblp:conf/cikm/ZieglerLS04 fatcat:r7iwwt3lung6dp4td3xzyhdgs4

Battling Predictability and Overconcentration in Recommender Systems

Sihem Amer-Yahia, Laks V. S. Lakshmanan, Sergei Vassilvitskii, Cong Yu
2009 IEEE Data Engineering Bulletin  
Recommendation Strategies Overview In this section, we review the two most basic recommendation approaches: item-based and collaborative filtering [3] .  ...  Collaborative filtering strategies broaden the scope of items being recommended to the user and have become increasingly popular.  ... 
dblp:journals/debu/Amer-YahiaLVY09 fatcat:r73zri4nx5cszkaaodcq5sxyce

Diversification in Session-based News Recommender Systems [article]

Alireza Gharahighehi, Celine Vens
2021 arXiv   pre-print
Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations.  ...  The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information.  ...  In the neighborhood-based SBRSs, the nearest neighbors convey the collaborative information and, according to the results, using more diverse collaborative in- Diversification in Session-based News Recommender  ... 
arXiv:2102.03265v1 fatcat:bzougwfvwvflheogd5x55m5ai4

Survey on Hybrid Recommendation System with Review Helpfulness Features

Patil Dhanashree T, Prof. Kakade Shital P
2017 IARJSET  
For performing recommendation there are different techniques like collaborative, content based, knowledge based and other techniques.  ...  In hybrid recommendation this methods are combined to improve the performance of recommendation.  ...  In different application area collaborative recommender have been implemented to improve recommendation topic diversification algorithm is used by Amazon.  ... 
doi:10.17148/iarjset/nciarcse.2017.08 fatcat:glsnewuq2vgfvpnec4hnuegdby

DiRec: Diversified recommendations for semantic-less Collaborative Filtering

Rubi Boim, Tova Milo, Slava Novgorodov
2011 2011 IEEE 27th International Conference on Data Engineering  
In this demo we present DiRec , a plug-in that allows Collaborative Filtering (CF) Recommender systems to diversify the recommendations that they present to users.  ...  We show the advantage of recommendation diversification and its feasibility even in the absence of semantic information.  ...  To overcome this difficulty, DiRec takes a different approach, inspired by work on Collaborative Filtering (CF) [3] .  ... 
doi:10.1109/icde.2011.5767942 dblp:conf/icde/BoimMN11 fatcat:kbz4pcl2yvbslf4xnnzcylviwa

In the Mood4

Rubi Boim, Tova Milo
2013 Proceedings of the 16th International Conference on Extending Database Technology - EDBT '13  
Mood4 utilizes the user's examples to refine the recommendations generated by a given recommender system, considering several, possibly competing, desired properties of the recommended items set (rating  ...  The system uses a novel algorithm, based on a simple geometric representation of the items, which allows for efficient processing and the generation of suitable recommendations even in the absence of semantic  ...  We then describe the geometric representation being used and explain how Collaborative Filtering.  ... 
doi:10.1145/2452376.2452463 dblp:conf/edbt/BoimM13 fatcat:rjdqgi5huje2dao2cs3qcmcmgy

Recommendation systems: Principles, methods and evaluation

F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh
2015 Egyptian Informatics Journal  
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  ...  On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload,  ...  Pros and Cons of collaborative filtering techniques Collaborative Filtering has some major advantages over CBF in that it can perform in domains where there is not much content associated with items and  ... 
doi:10.1016/j.eij.2015.06.005 fatcat:arp4euyhifhvppxf6z46rcuyqu

Nature Inspired Recommender Algorithms for Collaborative Web based Learning Environments

Dinesh KumarSaini, Lakshmi Sunil Prakash
2015 International Journal of Computer Applications  
Hybrid collaborative filtering is proposed for user and item attribute that can alleviate the sparsity issue in the recommender systems.  ...  Collaborative filtering is proposed for personalized recommendations; user and item attributes are used as filtration parameter.  ...  Hybrid recommendations (content based filtering and collaborative based filtering) were used in the recommendation phase.  ... 
doi:10.5120/20046-2048 fatcat:5huovase35eixlrgl4wnwsdraa

Domain Knowledge Based Personalized Recommendation Model and Its Application in Cross-selling

Lingling Zhang, Caifeng Hu, Quan Chen, Yibing Chen, Yong Shi
2012 Procedia Computer Science  
factor of profit when using traditional collaborative filtering as recommendation method.  ...  This paper proposes a new personalized recommendation model based on domain knowledge to emphasize the importance of domain knowledge in recommendation process.  ...  collaborative filtering (DCF) and collaborative filtering(CF).  ... 
doi:10.1016/j.procs.2012.04.144 fatcat:7gadzrhjbjhbzawicq7uom2czu
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