Determinantal Point Processes for Producing Diversified and Personalized Predictions
Creators: name: Liu, Yuli
Publisher: name: The Australian National University
Year: 2023

description: Producing personalized predictions, which filters out redundant information to meet a user's specific needs based on their historical interactions, is vital in online applications. Typically, these predictions are made within the recommender systems framework. Recently, the focus has expanded from individual elements to Temporal Sets Prediction (TSP) i.e., predicting subsequent sets given previous ones. However, most of existing models aim primarily to improve prediction accuracy, often resulting in monotonous suggestions. To enrich the suggestions and broaden users' horizons, diversifying personalized predictions has been a trending research problem. This thesis presents our efforts in incorporating diversification and personalization using the popular probabilistic model - Determinantal Point Processes (DPPs). First, we directly formulate the inherent goals of diversity-promoting recommendation as a two-objective optimization problem by simultaneously minimizing recommendation errors and maximizing diversity, which are integrated into Generative Adversarial Nets (GAN). In addition to the common practice of generating DPP-distributed diverse item subsets using MAP inference in the training process, a conditional DPP samples generation algorithm is applied in the serving (evaluation) procedure to ensure that the learned diversity-promoting recommendation model is capable of providing users with expected services. Next, we propose two objective functions for sequential recommendation that are both dependency-aware and diversity-aware, utilizing DPP-distributed likelihoods of standard DPPs and conditional DPPs, respectively. In contrast to commonly used objective functions (e.g., binary cross-entropy and pairwise ranking BPR), which primarily prioritize accuracy and are usually formulated under the assumption of independence among estimated items, our proposed objective functions for sequential recommendation stand out due to they explicitly consider dependencies and incorporate diversity as a guiding factor. Then, we find that probabilities of subsets with cardinality k learned from the specialized k-DPP distribution over a fixed size ground set enable us to compare and rank multiple items at the set level with consideration of both relevance and diversity. We implement the k-DPP based ranking in the context of both Matrix Factorization (MF) and neural networks approaches on three real-world datasets, obtaining improved relevance and diversity performances. The k-DPP ranking approach is broadly applicable, and when applied to seminal CF models it also yields strong performance improvements, suggesting that this technique holds significant value to the field of recommender systems. Last, associating the set-level predictions in the field of TSP with structures of structured DPP (SDPP), we present a Structured Temporal Sets Prediction framework (STSP), which makes two significant contributions. First, elements in a set of temporal sequence are directly modeled as a SDPP structure, in which a DPP distribution is modeled over collections of structures (temporal sets). Second, all trainable representations and underlying relationships are integrated into a dedicated sets-level objective function with the intention of enhancing the probability of selecting the desired subset of structures as a DPP. Accordingly, we treat temporal sets in a broader perspective compared to existing combination manners, enabling the underlying relationships to be explicitly represented. Altogether, we explore an interesting research task of producing diversified and personalized predictions. The intuitive design of dual consideration of diversity in both training and serving procedures, the noticed dependency across training sequence instances, the interesting discovery of k-DPP probability specified ranking implication, and the association between set-level predictions and SDPP structures hold significant value for further studies.