Recommender systems that include some reliability measure of their predictions tend to be more conservative in forecasting, due to their constraint to preserve reliability. This leads to a significant drop in the coverage and novelty that these systems can provide. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, which enables the control of the risk level desired when making decisions about the reliability of a prediction. Experimental results demonstrate that recklessness not only allows for risk regulation but also improves the quantity and quality of predictions provided by the recommender system.

Conversational agents are consistently growing in popularity and many people interact with them every day. While many conversational agents act as personal assistants, they can have many different goals. Some are task-oriented, such as providing customer support for a bank or making a reservation. Others are designed to be empathetic and to form emotional connections with the user. The Alexa Prize Challenge aims to create a socialbot, which allows the user to engage in coherent conversations, on a range of popular topics that will interest the user. Here we describe Athena 2.0, UCSC's conversational agent for Amazon's Socialbot Grand Challenge 4. Athena 2.0 utilizes a novel knowledge-grounded discourse model that tracks the entity links that Athena introduces into the dialogue, and uses them to constrain named-entity recognition and linking, and coreference resolution. Athena 2.0 also relies on a user model to personalize topic selection and other aspects of the conversation to individual users.

This paper presents a retrospective overview of a decade of research in our department towards self-organizing personal knowledge assistants in evolving corporate memories. Our research is typically inspired by real-world problems and often conducted in interdisciplinary collaborations with research and industry partners. We summarize past experiments and results comprising topics like various ways of knowledge graph construction in corporate and personal settings, Managed Forgetting and (Self-organizing) Context Spaces as a novel approach to Personal Information Management (PIM) and knowledge work support. Past results are complemented by an overview of related work and some of our latest findings not published so far. Last, we give an overview of our related industry use cases including a detailed look into CoMem, a Corporate Memory based on our presented research already in productive use and providing challenges for further research. Many contributions are only first steps in new directions with still a lot of untapped potential, especially with regard to further increasing the automation in PIM and knowledge work support.

In recent years, multiple notions of algorithmic fairness have arisen. One such notion is individual fairness (IF), which requires that individuals who are similar receive similar treatment. In parallel, matrix estimation (ME) has emerged as a natural paradigm for handling noisy data with missing values. In this work, we connect the two concepts. We show that pre-processing data using ME can improve an algorithm's IF without sacrificing performance. Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions. We then show that, under analogous conditions, SVT pre-processing also yields estimates that are consistent and approximately minimax optimal. As such, the ME pre-processing step does not, under the stated conditions, increase the prediction error of the base algorithm, i.e., does not impose a fairness-performance trade-off. We verify these results on synthetic and real data.

Personalized review-based rating prediction aims at leveraging existing reviews to model user interests and item characteristics for rating prediction. Most of the existing studies mainly encounter two issues. First, the rich knowledge contained in the fine-grained aspects of each review and the knowledge graph is rarely considered to complement the pure text for better modeling user-item interactions. Second, the power of pre-trained language models is not carefully studied for personalized review-based rating prediction. To address these issues, we propose an approach named Knowledge-aware Collaborative Filtering with Pre-trained Language Model (KCF-PLM). For the first issue, to utilize rich knowledge, KCF-PLM develops a transformer network to model the interactions of the extracted aspects w.r.t. a user-item pair. For the second issue, to better represent users and items, KCF-PLM takes all the historical reviews of a user or an item as input to pre-trained language models. Moreover, KCF-PLM integrates the transformer network and the pre-trained language models through representation propagation on the knowledge graph and user-item guided attention of the aspect representations. Thus KCF-PLM combines review text, aspect, knowledge graph, and pre-trained language models together for review-based rating prediction. We conduct comprehensive experiments on several public datasets, demonstrating the effectiveness of KCF-PLM.

Next basket recommendation (NBR) is the task of predicting the next set of items based on a sequence of already purchased baskets. It is a recommendation task that has been widely studied, especially in the context of grocery shopping. In next basket recommendation (NBR), it is useful to distinguish between repeat items, i.e., items that a user has consumed before, and explore items, i.e., items that a user has not consumed before. Most NBR work either ignores this distinction or focuses on repeat items. We formulate the next novel basket recommendation (NNBR) task, i.e., the task of recommending a basket that only consists of novel items, which is valuable for both real-world application and NBR evaluation. We evaluate how existing NBR methods perform on the NNBR task and find that, so far, limited progress has been made w.r.t. the NNBR task. To address the NNBR task, we propose a simple bi-directional transformer basket recommendation model (BTBR), which is focused on directly modeling item-to-item correlations within and across baskets instead of learning complex basket representations. To properly train BTBR, we propose and investigate several masking strategies and training objectives: (i) item-level random masking, (ii) item-level select masking, (iii) basket-level all masking, (iv) basket-level explore masking, and (v) joint masking. In addition, an item-basket swapping strategy is proposed to enrich the item interactions within the same baskets. We conduct extensive experiments on three open datasets with various characteristics. The results demonstrate the effectiveness of BTBR and our masking and swapping strategies for the NNBR task. BTBR with a properly selected masking and swapping strategy can substantially improve NNBR performance.

Sequential fundraising in two sided online platforms enable peer to peer lending by sequentially bringing potential contributors, each of whose decisions impact other contributors in the market. However, understanding the dynamics of sequential contributions in online platforms for peer lending has been an open ended research question. The centralized investment mechanism in these platforms makes it difficult to understand the implicit competition that borrowers face from a single lender at any point in time. Matching markets are a model of pairing agents where the preferences of agents from both sides in terms of their preferred pairing for transactions can allow to decentralize the market. We study investment designs in two sided platforms using matching markets when the investors or lenders also face restrictions on the investments based on borrower preferences. This situation creates an implicit competition among the lenders in addition to the existing borrower competition, especially when the lenders are uncertain about their standing in the market and thereby the probability of their investments being accepted or the borrower loan requests for projects reaching the reserve price. We devise a technique based on sequential decision making that allows the lenders to adjust their choices based on the dynamics of uncertainty from competition over time. We simulate two sided market matchings in a sequential decision framework and show the dynamics of the lender regret amassed compared to the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps.

Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings. This assumption is particularly problematic in finance, where financial products exhibit continuous changes in valuations, leading to frequent shifts in client interests. These evolving interests, summarized in the past client-product interactions, see their utility fade over time with a degree that might differ from one client to another. To address this challenge, we propose a time-dependent collaborative filtering algorithm that can adaptively discount distant client-product interactions using personalized decay functions. Our approach is designed to handle the non-stationarity of financial data and produce reliable recommendations by modeling the dynamic collaborative signals between clients and products. We evaluate our method using a proprietary dataset from BNP Paribas and demonstrate significant improvements over state-of-the-art benchmarks from relevant literature. Our findings emphasize the importance of incorporating time explicitly in the model to enhance the accuracy of financial product recommendation.

Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.

These groundbreaking models are designed to represent users and items as a bipartite, undirected graph, unlocking a whole new level of high-order relationships that were previously almost unattainable. Not only they do achieve better accuracy than their predecessors, but they are also setting a new standard for modern recommender systems [20, 28, 47, 79]. In recent years, great effort has been devoted in creating GNN-based models that address the critical issues of existing models, such as the over-smoothing phenomenon [12] and scalability issues [87]. These cutting-edge models are taking the world of recommender systems by storm and ushering in a new era of accuracy [41, 47, 51, 59, 81]. Over the past ten years, the application of neural techniques rooted in graph representation learning, such as graph convolutional networks [35] (GCNs), has introduced a fresh perspective on traditional collaborative filtering (CF) approaches. Rather than relying solely on user-item interactions for optimization [29, 36, 55], GCN-based methods enable the extraction of both short-and long-distance user preferences toward items [71]. By incorporating multi-hop relationships into the embeddings of users and items, these learned profiles yield more precise recommendations, as evidenced in the literature [28, 47].