MOPO-LSI is an open-source Multi-Objective Portfolio Optimization Library for Sustainable Investments. This document provides a user guide for MOPO-LSI version 1.0, including problem setup, workflow and the hyper-parameters in configurations.

Recommender system has been demonstrated as a successful solution to assist decision makings. Context-awareness becomes necessity in recommendations, especially in mobile computing, since a user's decision may vary from contexts to contexts. Context-aware recommender systems, therefore, emerged to adapt the personalizations to different contextual situations. Context filtering is one of the popular ways to develop the context-aware recommendation models. Contextual pre-filtering techniques have been well developed, but the post-filtering methods are still under investigated. In this paper, we propose a simple but effective post-filtering recommendation approach. We demonstrate the effectiveness of this algorithm in comparison with other context-aware recommendation approaches based on the real-world rating data from mobile applications. Our experimental results reveal that the proposed algorithm is the best post-filtering approach, and it is even able to outperform the popular pre-filtering and contextual modeling recommendation models.