Changes between Version 4 and Version 5 of RelatedWork


Ignore:
Timestamp:
09/28/08 21:56:15 (17 years ago)
Author:
fmittag
Comment:

added comments about most of the related work

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  • RelatedWork

    v4 v5  
    66 
    77A Survey of Trust and Reputation Systems for Online Service Provision 
    8 http://www.oasis-open.org/committees/download.php/28303/JIB2007-DSS-Survey.pdf 
     8[http://www.oasis-open.org/committees/download.php/28303/JIB2007-DSS-Survey.pdf Link] 
    99 
    1010== Collaborative Filtering == 
     11(in order of estimated relevance) 
     12 
     13Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms 
     14[http://citeseer.ist.psu.edu/papagelis05incremental.html Link] [[BR]] 
     15user-similarity, Pearson-correlation, incremental algorithm 
     16 
     17Trust-aware Collaborative Filtering for Recommender Systems 
     18[http://sra.itc.it/people/massa/publications/massa_paolo_coopis_2004_trust-aware_Collaborative_Filtering_for_Recommender_Systems.pdf Link] [[BR]] 
     19Pearson-correlation, explicit trust, web of trust, decentralized 
     20 
     21On User Recommendations Based on Multiple Cues 
     22[http://citeseer.ist.psu.edu/dudek03user.html Link] [[BR]] 
     23user similarity, Pearson-correlation, semantic features, user-specified features 
     24 
     25Item-based Collaborative Filtering Recommendation Algorithms 
     26[http://citeseer.ist.psu.edu/sarwar01itembased.html Link] [[BR]] 
     27comparison of item-based recommendation algorithms (performance, quality, similarity) using Pearson-correlation, cosine-similarity, regression 
     28 
     29Trust in Recommender Systems 
     30[http://portal.acm.org/ft_gateway.cfm?id=1040870&type=pdf&coll=GUIDE&dl=GUIDE&CFID=58637181&CFTOKEN=67159970 Link] 
     31 
     32Content-Based versus Collaborative Filtering 
     33[http://www.is-frankfurt.de/uploads/down417.pdf Link] [[BR]] 
     34overview, references (Seminararbeit) 
     35 
     36Slope One Predictors for Online Rating-Based Collaborative Filtering 
     37[http://citeseer.ist.psu.edu/lemire05slope.html Link] [[BR]] 
     38slope one predictor, quick cold-start 
    1139 
    1240Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems 
    13 http://ict.ewi.tudelft.nl/pub/jun/sac06.pdf 
     41[http://ict.ewi.tudelft.nl/pub/jun/sac06.pdf Link] [[BR]] 
     42distributed collaborative filtering, p2p, bayesian 
    1443 
    1544Collaborative Filtering with Privacy via Factor Analysis 
    16 http://www.cs.berkeley.edu/~jfc/'mender/sigir.pdf 
    17  
    18 Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms 
    19 http://citeseer.ist.psu.edu/papagelis05incremental.html 
    20  
    21 Item-based Collaborative Filtering Recommendation Algorithms 
    22 http://citeseer.ist.psu.edu/sarwar01itembased.html 
    23  
    24 On User Recommendations Based on Multiple Cues 
    25 http://citeseer.ist.psu.edu/dudek03user.html 
    26  
    27 Slope One Predictors for Online Rating-Based Collaborative Filtering 
    28 http://citeseer.ist.psu.edu/lemire05slope.html 
    29  
    30 Trust in Recommender Systems 
    31 http://portal.acm.org/ft_gateway.cfm?id=1040870&type=pdf&coll=GUIDE&dl=GUIDE&CFID=58637181&CFTOKEN=67159970 
    32  
    33 Trust-aware Collaborative Filtering for Recommender Systems 
    34 http://sra.itc.it/people/massa/publications/massa_paolo_coopis_2004_trust-aware_Collaborative_Filtering_for_Recommender_Systems.pdf 
    35  
    36 Content-Based versus Collaborative Filtering 
    37 http://www.is-frankfurt.de/uploads/down417.pdf 
     45[http://www.cs.berkeley.edu/~jfc/'mender/sigir.pdf Link] [[BR]] 
     46expectation maximization (EM), privacy 
    3847 
    3948 
    4049== Content-based == 
    4150 
    42  * Content-based, Collaborative Recommendation ([http://portal.acm.org/citation.cfm?doid=245108.245124 Link]) 
    43  * Improving Interoperability using Query Interpretation in Semantic Vector Spaces ([http://www.springerlink.com/content/r3610g058065m17q/ Link]) 
    44    * The aim of this paper is providing some interoperability between several ontologies for queries. However, as many of our Features also are interrelated/similar, even in the same ontology, we need a similar query expansion technique for computing Feature/Item similarity. 
     51Content-based, Collaborative Recommendation ([http://portal.acm.org/citation.cfm?doid=245108.245124 Link]) [[BR]] 
     52no algorithms given, irrelevant 
     53 
     54Improving Interoperability using Query Interpretation in Semantic Vector Spaces ([http://www.springerlink.com/content/r3610g058065m17q/ Link]) [[BR]] 
     55The aim of this paper is providing some interoperability between several ontologies for queries. However, as many of our Features also are interrelated/similar, even in the same ontology, we need a similar query expansion technique for computing Feature/Item similarity. 
    4556 
    4657 
    4758== Context == 
    4859Using Semantic Cues for Contextual Recommendation (1997) 
    49 http://clinton.cs.depaul.edu/upe/CTIRS/papers/Ramezani.pdf 
     60[http://clinton.cs.depaul.edu/upe/CTIRS/papers/Ramezani.pdf Link] [[BR]] 
     61Pearson-correlation