Sun, 27 Jul 2008 07:27:29 BST CiteULike: pdlug's recommendation http://www.citeulike.org/user/pdlug/tag/recommendation CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/pdlug/article/2841716 <i>(27 May 2008)</i><br /><br />In this paper, based on a weighted projection of bipartite user-object network, we introduce a personal recommendation algorithm which has remarkably higher accuracy than the classical algorithm, namely collaborative filtering. In this algorithm, the correlation resulting from a specific attribute may be repeatedly counted in the cumulative recommendations from different objects. By considering the higher order correlations, we design an effective algorithm that can, to some extent, eliminate the redundant correlations. The algorithmic accuracy, measured by the ranking score, can be further improved by 23% in the optimal case. Most of the previous studies considered the algorithmic accuracy only, in this paper, we argue that the diversity and popularity, as two significant criteria of algorithmic performance, should also be taken into account. With more or less the same accuracy, an algorithm giving higher diversity and lower popularity is more favorable. Numerical results show that the present algorithm can outperform the standard one simultaneously in all three criteria: higher accuracy, higher diversity, and lower popularity. Ultra accurate personal recommendation via eliminating redundant correlations Tao Zhou Riqi Su Runran Liu Luoluo Jiang Bing-Hong Wang Yi-Cheng Zhang (27 May 2008) 2008-05-28T14:54:51-00:00 2008 collaborative collaborative-filtering filtering recommendation http://www.citeulike.org/user/pdlug/article/2774250 <i>(21 April 2008)</i><br /><br />The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from CiteSeer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method. Learning Multiple Graphs for Document Recommendations Ding Zhou Shenghuo Zhu Kai Yu Xiaodan Song Belle Tseng Hongyuan Zha Lee Giles( (21 April 2008) 2008-05-09T04:14:29-00:00 2008 graph learning machine-learning network recommendation http://www.citeulike.org/user/pdlug/article/2719467 <i>(8 May 2007)</i><br /><br />Several approaches to collaborative filtering have been studied but seldom have the studies been reported for large (several millions of users and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptible for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News. Google News Personalization: Scalable Online Collaborative Filtering Abhinandan Das Mayur Datar Ashutosh Garg Shyam Rajaram (8 May 2007) 2008-04-25T21:00:08-00:00 2007 collaborative-filtering filtering google machine-learning mapreduce recommendation http://www.citeulike.org/user/pdlug/article/1217995 <i>(2005)</i><br /><br />Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks. We investigate a large person-to-person recommendation network, consisting of four million people... Patterns of influence in a recommendation network J Leskovec A Singh J Kleinberg (2005) 2007-04-09T15:40:22-00:00 2005 collaborative collaborative-filtering network pattern patterns recommendation social socialnetwork social-network