Social commerce platforms are emerging businesses where producers sell products through re-sellers who advertise the products to other customers in their social network. Due to the increasing popularity of this business model, thousands of small producers and re-sellers are starting to depend on these platforms for their livelihood; thus, it is important to provide fair earning opportunities to them. The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products.

Many users of e-commerce portals commonly use customer reviews for making purchase decisions. But a product may have tens or hundreds of diverse reviews leading to information overload on the customer. The main objective of our work is to develop a recommendation system to recommend a subset of reviews that have high content score and good coverage over different aspects of the product along with their associated sentiments. We address the challenge which arises due to the fact that similar aspects are mentioned in different reviews using different natural language expressions. We use vector representations to identify mentions of similar aspects and map them with aspects mentioned in product features specifications. Review helpfulness score may act as a proxy for the quality of reviews, but new reviews do not have any helpfulness score. We address the cold start problem by using a dynamic convolutional neural network to estimate the quality score from review content. The system is evaluated on datasets from Amazon and Flipkart and is found to be more effective than the competing methods.