Created by W.Langdon from gp-bibliography.bib Revision:1.5628
To remedy the sparsity problems we propose methods to enrich the set of user connections obtained using measures such as Pearson Correlation Coefficient (PCC) and Cosine Similarity (COS). We achieve this by leveraging on explicit trust elicitation and trust transitivity. When interacting with anonymous users online, in the absence of physical cues apparent in our daily life, trust provides a reliable measure of quality and guides the user decision process on whether or not to interact with an entity. These trust statements in addition to identifying malicious users also enhance user connectivity by establishing links between pairs of users whose closeness cannot be determined through preference data. In addition transitivity of trust can also be leveraged to further expand the set of neighbours to collaborate with. We first explore a bifurcated view of trust: functional and referral trust i.e. trust in an entity to recommend items and the trust in an entity to recommend recommenders and propose models to quantify referral trust. Such a referral-functional trust framework leads to more meaningful derivation of trust through transitivity resulting in better quality recommendations.
Though trust has been extensively used in literature to support the CF process, distrust information has been explored very little in this context. We thus propose a tri-component computation of trust and distrust using preference, functional trust and referral trust in order to densify the network of user interconnections. To maintain a balance between increased coverage and the quality of recommendations, however, we quantify risk measures for each trust and distrust relationship so derived and prune the network to retain high quality relationships thus ensuring good connections formed between users through transitivity of trust and distrust.
In the absence of supplemental information such as trust/distrust to provide extra knowledge about user links the local similarity connections can be harnessed to deem a pair of users similar if they are share preferences with the same set of users thus estimating the global similarity between user pairs. We investigate the effectiveness of various graph based global or indirect similarity computation schemes in enhancing the user or item neighborhood thus bettering the quality and number of recommendations obtained.",
Finally sparsity variant fusion of predictions from local and global similarity measures have been shown to offer quality recommendations. In particular the fact that local similarity measures suffice when the preference data is dense but overtaken in performance by global similarity links when preference data is scarce can be leveraged to fuse the recommendations from the two systems. We define sparsity not only for the overall system but also at the user and user-item level. We use these measures to suggest a fusion scheme tailored for each user and/or for each item to be predicted by estimating the apportionment of influence local and global similarity measures have on each prediction.
We demonstrate the effectiveness of the proposed techniques through experiments performed on real world datasets.",
Genetic Programming entries for Deepa Anand