<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ekstrand, M. D.</style></author><author><style face="normal" font="default" size="100%">Kannan, P.</style></author><author><style face="normal" font="default" size="100%">Stemper, J. A.</style></author><author><style face="normal" font="default" size="100%">J. T. Butler</style></author><author><style face="normal" font="default" size="100%">Konstan, J. A.</style></author><author><style face="normal" font="default" size="100%">Riedl, J. T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatically Building Research Reading Lists</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Recommender Systems Conference </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">citation web</style></keyword><keyword><style  face="normal" font="default" size="100%">collaborative filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">digital libraries</style></keyword><keyword><style  face="normal" font="default" size="100%">recommender systems</style></keyword><keyword><style  face="normal" font="default" size="100%">user study</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2010</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">http://grouplens.org/system/files/recsys2010.beamer.pdf</style></url><url><style face="normal" font="default" size="100%">http://grouplens.org/system/files/reading-lists.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">Barcelona, Spain</style></pub-location><abstract><style face="normal" font="default" size="100%">All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an online analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content- based approaches for generating introductory reading lists.</style></abstract></record></records></xml>