A number of recent studies have proposed new recommender designs that incorporate firm-centric measures (e.g., the profit margins of products) along with consumer-centric measures (e.g., relevance of recommended products). These designs seek to maximize the long-term profits from recommender deployment without compromising customer trust. However, very little is known about how consumers might respond to recommender algorithms that account for product profitability. We tested the impact of deploying a profit-based recommender on its precision and usage, as well as customer purchasing and trust, with data from an online randomized field experiment. We found that the profit-based algorithm, despite potential concerns about its negative impact on consumers, is effective in retaining consumers’ usage and purchase levels at the same rate as a content-based recommender. We also found that the profit-based algorithm generated higher profits for the firm. Further, to measure trust, we issued a post-experiment survey to participants in the experiment; we found there were no significant differences in trust across treatment. We related the survey results to the accuracy and diversity of recommendations and found that accuracy and diversity were both positively and significantly related to trust. The study has broader implications for firms using recommenders as a marketing tool, in that the approach successfully addresses the relevance-profit tradeoff in a real-world context.