Australia’s largest bank, Commonwealth Bank (CBA) has a large data and analytics function that focuses on building a brighter future for all using data and decision science. In this work, we focus on creating better services for CBA customers by developing a next generation recommender system that brings the most relevant merchant reward offers that can help customers save money. Our recommender provides CBA cardholders with cashback offers from merchants, who have different objectives when they create offers. This work describes a multi-stakeholder, multi-objective problem in the context of CommBank Rewards (CBR) and describes how we developed a system that balances the objectives of the bank, its customers, and the many objectives from merchants into a single recommender system.

This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.

In this paper, we summarize VAMS 2017 - a workshop on value-aware and multistakeholder recommendation co-located with RecSys 2017. The workshop encouraged forward-thinking papers in this new area of recommender systems research and obtained a diverse set of responses ranging from application results to research overviews.

People recommenders have become a rich research area within the broad recommender systems community and social recommender systems in particular. From "people you may know" and "who to follow" widgets, through people introduction at conferences, job recommendations and job-candidate search, to dating partner matchmakers, people recommendations proliferate. This tutorial will present an overview of the people recommender systems domain. We will present the different types and use cases of people recommendations, the special techniques used to recommend people to themselves, key research work, and open challenges.

While Recommender Systems are powerful drivers of engagement and transactional utility in social networks, People recommenders are a fairly involved and diverse subdomain. Consider that movies are recommended to be watched, news is recommended to be read, people however, are recommended for a plethora of reasons -- such as recommendation of people to befriend, follow, partner, targets for an advertisement or service, recruiting, partnering romantically and to join thematic interest groups.

The bias towards popular items is not necessarily an undesired outcome of recommender algorithms since a large amount of revenue on e-commerce websites is drawn from these popular items. On the other hand, in domains such as online dating and employment websites, where users and items of the recommendation are both people, a strong bias towards popular users may cause these users to feel overwhelmed and unpopular users to feel neglected. In this paper, we use collaborative filtering (CF) to generate recommendations for all users, and by using stochastic matching we select a number of reciprocal recommendations for each user that maximizes the matches among all users. In this way, all users, regardless of their popularity, will receive the same number of recommendations the number of times they will be recommended to others. This study is the first to apply a stochastic matching solution to balance the number of recommendations given to users in a CF setting. Using historical data, we demonstrate that the proposed recommender improves the chance of finding a successful relationship in comparison to CF recommendations.

The reciprocal recommender is a class of recommender system that is important for several tasks where people are both the subjects and objects of the recommendation. Some examples are: job recommendation, mentor-mentee matching, and online dating. Despite the importance of this type of recommender, our work is the first to distinguish it and define its properties. We have implemented RECON, a reciprocal recommender for online dating, and have evaluated it on a large dataset from a major Australian dating website. We investigated the predictive power gained by taking account of reciprocity, finding that it is substantial, for example it improved the success rate of the top ten recommendations from 23% to 42% and also improved the recall at the same time. We also found reciprocity to help with the cold start problem obtaining a success rate of 26% for the top ten recommendations for new users. We discuss the implications of these results for broader uses of our approach for other reciprocal recommenders.

Reciprocal recommender is a class of recommender systems that is important for tasks where people are both the subject and the object of the recommendation; one such task is online dating. We have implemented RECON, a reciprocal recommender for online dating, and we have evaluated it on a major dating website. Results show an improved success rate for recommendations that consider reciprocity in comparison to recommendations that only consider the preferences of the users receiving the recommendations.