No abstract

Trust-aware recommender systems are intelligent technology applications that make use of trust information and user personal data in social networks to provide personalized recommendations. Earlier research in trust-aware systems have shown that the ability of trust-based systems to make accurate predictions coupled with their robustness from shilling attacks make them a better alternative than traditional recommender systems. In this paper we propose an approach for improving accuracy of predictions in trust-aware recommender systems. In our approach, we first reconstruct the trust network. Trust network is reconstructed by removing trust links between users having correlation coefficient below a specified threshold value. For prediction calculation we compare three different approaches based on trust and correlation. We show through experiments on real life Epinions data set that our proposed approach of reconstructing the trust network gives substantially better prediction accuracy than the original approach of using all trust statements in the network.

No abstract

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 27 19a. NAME OF RESPONSIBLE PERSON

Two new marking algorithms for AND/OR graphs called CF and CS are presented. For admissible heuristics CS is not needed, and CF is shown to be preferable to the marking algorithms of Martelli and Montanari. When the heuristic is not admissible, the analysis is carried out with the help of the notion of the first and second discriminants of an AND/OR graph. It is proved that in this case CF can be followed by CS to get optimal solutions, provided the sumcost criterion is used and the first discriminant equals the second. Estimates of time and storage requirements are given. Other cost measures, such as maxcost, are also considered, and a number of interesting open problems are enumerated.