SpectR*: Expander Graph Based Seller Recommender for Mitigating Cold Start Loss in Larger Facebook Market

The enormous growth of the Facebook marketplace is prompting sellers to leverage the network to enhance their business. The pandemic has introduced more cold start sellers on Facebook, and the buyers' pandemic needs have made imperfect observations on seller Item quality. Users' trust in the sellers varies from quality to quality and from point to point, and thereby could lead to a loss from the cold start sellers. The need of the hour is an approach which could walk along the paths of strongly connected graphs to analyse products brought by the users, and thereby, better recommendations can be built. This article presents a new graph based method, the SpectR*, to recommend a seller to other Facebook business groups. Due to the demographic structural differences, larger networks have smaller expansion capabilities, which is why many recommender systems failed on graph networks like Facebook and Twitter, to analyse the reviews from the users. Our proposed work is based on constructing an Expander Graph on Facebook user network, based on calculated Cheegers constant. We used this Graph theory based Cheegers constant as measure for deriving well connected Facebook family of Graphs on which the product reviews can be analysed.