In today's competitive market, mobile service providers are very keen on improving the customer satisfaction by providing personalized services. Recommending recharge packs to the subscribers that suits their personal profile is an important such personalized service. But a solution to this problem is not that simple as it requires careful analysis of the subscribers' usage behavior and involves very large volume of data generated by the subscribers' frequent interaction with the telecom network. Also, this solution needs to ensure a fine balance between customer satisfaction and profitability of service providers. This paper discusses about an adaptive recommendation model which overcomes various deficiencies associated with existing solutions. The model recommends suitable recharge packs to subscribers based on their usage history and affordability. Further, it accommodates a configurable fairness parameter that ensures a balance between the profitability factor, conversion probability and relevance of the recommendations. Due to the sheer volume of the data involved, the model is implemented using a distributed framework. The validity of the model is evaluated on the basis of statistical properties and conversion factor. © 2013 IEEE.