DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation

In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music perception is that music is experienced in temporal context and in sequence. In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions. The model is learned online and is uniquely adapted for each listener. To reduce exploration time, DJ-MC exploits user feedback to initialize a model, which it subsequently updates by reinforcement. We evaluate our framework with human participants using both real song and playlist data. Our results indicate that DJ-MC's ability to recommend sequences of songs provides a significant improvement over more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems (AAMAS) 2015, Istanbul, Turkey, May 201

An integrated MCDA software application for forest planning : a case study in southwestern Sweden

Forest planning in Sweden today translates not only into planning of timber production, but also for the provision of other functions and services. Multi-criteria decision analysis (MCDA) methods provide a way to take also non-monetary values into account in planning. The purpose of this study was to gain experience on how to use a forest decision support system combined with an MCDA tool in practical forestry. We used a new forest planning tool, PlanWise, which includes an integrated MCDA module, PlanEval. Using the software, the decision maker can compare different forest plans and evaluate them against his/her objectives in a structured and analytical manner. The analysis thus provides a ranking of the alternatives based on the individual preferences of the decision maker. PlanEval and the MCDA planning process are described in a case study, where the manager of a forest estate in southwestern Sweden used the program to compare different forest plans made for the estate. In the paper, we analyze possibilities and challenges of this approach and identify problems such as the adherence to formal requirements of MCDA techniques and the difficulty of comparing maps. Possibilities to expedite an MCDA planning process further are also discussed. The findings confirm that integration of an MCDA tool with a forest decision support system is valuable, but requires expert assistance to be successful