In today's era of electronic markets with information overload, generating personalized recommendations for e-commerce users is a challenging and interesting problem. Recommending top-N items of interest to e-commerce users is more challenging using binary implicit feedback. The training data is usually highly sparse and has binary values capturing a user's action or inaction. Due to the sparseness of data and lack of explicit user preferences, neighborhood-based and model-based approaches may not be effective to generate accurate recommendations. Of late, network-based item recommendation methods, which utilize item related meta-information, have started getting attention. In this work, we propose a heterogeneous information network-based recommendation model called HeteroPRS for personalized top-N recommendations using binary implicit feedback. To utilize the potential of meta-information related to items, we use the concept of meta-path. To improve the effectiveness of the recommendations, the popularity of items and interest of users are leveraged simultaneously. Personalized weight learning of various meta-paths in the network is performed to determine the intrinsic interests of users from the binary implicit feedback. The proposed model is experimentally evaluated and compared with various recommendation techniques for implicit feedback using real-world datasets, and the results show the effectiveness of the proposed model.