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Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem
Aug. 2012 (vol. 24 no. 8)
pp. 1478-1490
ASCII Text
x 
 
P. Tamayo, M. F. Hornick, "Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 8, pp. 1478-1490, Aug., 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2011.90,
author = {P. Tamayo and M. F. Hornick},
title = {Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {8},
issn = {1041-4347},
year = {2012},
pages = {1478-1490},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.90},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem
IS - 8
SN - 1041-4347
SP1478
EP1490
EPD - 1478-1490
A1 - P. Tamayo,
A1 - M. F. Hornick,
PY - 2012
KW - sparse matrices
KW - collaborative filtering
KW - data mining
KW - matrix decomposition
KW - recommender systems
KW - Oracle OpenWorld conference
KW - disjoint user-item sets
KW - conference sessions
KW - recommendation engine
KW - conjoint decomposition of item and users
KW - preference-based recommender systems
KW - model-based recommender systems
KW - known usage behavior
KW - item matrices
KW - item domain
KW - user matrices
KW - user behavior
KW - RECONDITUS paradigm
KW - data mining problem
KW - Matrix decomposition
KW - Recommender systems
KW - Collaboration
KW - Abstracts
KW - Data models
KW - Data mining
KW - Sparse matrices
KW - data mining.
KW - Recommender systems
KW - collaborative filtering
KW - extensions to recommender systems
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
P. Tamayo, Oracle Corp., Burlington, MA, USA
M. F. Hornick, Oracle Corp., Burlington, MA, USA
In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional model-based recommender systems, as suggested in Adomavicius and Tuzhilin [CHECK END OF SENTENCE], can address this problem. We introduce Recommendation Engine by CONjoint Decomposition of ITems and USers (RECONDITUS)-a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set. The assumption being that preferences exhibited by users with known usage behavior (e.g., past conference session attendance), which can be abstracted by projections of user and item matrices, will be similar to ones of new (different) users where the basic environment and item domain are the same (e.g., new conference). RECONDITUS requires no item ratings, but operates on observed user behavior such as past conference session attendance. The RECONDITUS paradigm consists of projections of both user and item data, and the learning of relationships in projected space. Once established, the relationships enable predicting new relationships and provide associated recommendations. The approach can encompass several traditional data mining problems where both clustering and prediction are necessary. RECONDITUS has been evaluated using data from the Oracle OpenWorld conference.

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Index Terms:
sparse matrices,collaborative filtering,data mining,matrix decomposition,recommender systems,Oracle OpenWorld conference,disjoint user-item sets,conference sessions,recommendation engine,conjoint decomposition of item and users,preference-based recommender systems,model-based recommender systems,known usage behavior,item matrices,item domain,user matrices,user behavior,RECONDITUS paradigm,data mining problem,Matrix decomposition,Recommender systems,Collaboration,Abstracts,Data models,Data mining,Sparse matrices,data mining.,Recommender systems,collaborative filtering,extensions to recommender systems
Citation:
P. Tamayo, M. F. Hornick, "Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 8, pp. 1478-1490, Aug. 2012, doi:10.1109/TKDE.2011.90
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