The use of recommender systems to assist in the provision of financial asset and portfolio recommendations to investors is increasing, spanning a wide range of algorithms and techniques. Several strategies have been devised for the evaluation of financial asset recommendations, with the two most prominent perspectives measuring, respectively, (a) the money customers could obtain if they followed the recommendations (profitability-based evaluation) and (b) the ability of models to predict future customer investments (transaction-based evaluation). If customers are effective investors, we would expect these two perspectives to be positively correlated. In this article, we explore the actual relationship between these two families of metrics. Theoretically, we prove that these perspectives are independent. Furthermore, we perform experiments over a large-scale financial recommendation dataset with real customer investment transactions. Surprisingly, we find that transaction and profitability-based metrics are, in fact, negatively correlated. Moreover, algorithms that actively learn from past customer transactions might lose money in the mid-term. A thorough analysis of model performance and customer transaction patterns over time shows that this is due to customers failing to consistently beat the market with their investments, with time appearing as an important confounding variable—since the point of time where recommendations are provided and the investment horizon largely affect the customer’s investment performance.

Information retrieval (IR) plays a critical role in financial decision-making across investment research, trading, risk management, and reporting. With the rise of large language models (LLMs), IR systems have evolved to support more natural, context-aware workflows. In this tutorial, we survey recent advances in applying IR and LLM technologies in finance, covering agent-based simulations, investor recommender systems, retrieval-augmented research management, and LLM-driven portfolio construction. We highlight practical challenges and propose future research directions at the intersection of IR, LLMs, and financial innovation. More materials can be found at http://irfin.nlpfin.com/.

Large language model-based agents are becoming increasingly popular as a low-cost mechanism to provide personalized, conversational advice, and have demonstrated impressive capabilities in relatively simple scenarios, such as movie recommendations. But how do these agents perform in complex high-stakes domains, where domain expertise is essential and mistakes carry substantial risk? This paper investigates the effectiveness of LLM-advisors in the finance domain, focusing on three distinct challenges: (1) eliciting user preferences when users themselves may be unsure of their needs (2) providing personalized guidance for diverse investment preferences, and (3) leveraging advisor personality to build relationships and foster trust. Via a lab-based user study with 64 participants, we show that LLM-advisors often match human advisor performance when eliciting preferences, although they can struggle to resolve conflicting user needs. When providing personalized advice, the LLM was able to positively influence user behavior, but demonstrated clear failure modes. Our results show that accurate preference elicitation is key, otherwise, the LLM-advisor has little impact, or can even direct the investor toward unsuitable assets. More worryingly, users appear insensitive to the quality of advice being given, or worse these can have an inverse relationship. Indeed, users reported a preference for and increased satisfaction as well as emotional trust with LLMs adopting an extroverted persona, even though those agents provided worse advice.

Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON.

The cyclic nature of the recommendation task is being increasingly taken into account in recommender systems research. In this line, framing interactive recommendation as a genuine reinforcement learning problem, multi-armed bandit approaches have been increasingly considered as a means to cope with the dual exploitation/exploration goal of recommendation. In this paper we develop a simple multi-armed bandit elaboration of neighbor-based collaborative filtering. The approach can be seen as a variant of the nearest-neighbors scheme, but endowed with a controlled stochastic exploration capability of the users' neighborhood, by a parameter-free application of Thompson sampling. Our approach is based on a formal development and a reasonably simple design, whereby it aims to be easy to reproduce and further elaborate upon. We report experiments using datasets from different domains showing that neighbor-based bandits indeed achieve recommendation accuracy enhancements in the mid to long run.

Contact recommendation has become a common functionality in online social platforms, and an established research topic in the social networks and recommender systems fields. Predicting and recommending links has been mainly addressed to date as an accuracy-targeting problem. In this paper we put forward a different perspective, considering that correctly predicted links may not be all equally valuable. Contact recommendation brings an opportunity to drive the structural evolution of a social network towards desirable properties of the network as a whole, beyond the sum of the isolated gains for the individual users to whom recommendations are delivered -global properties that we may want to assess and promote as explicit recommendation targets.