A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering

In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number of resources (users or items) that can be allocated and related to them. The solution requires a trade-off between exploitation and exploration as with the limited recommendation opportunities, we need to, on one hand, allocate the most relevant resources right away, but, on the other hand, it is also necessary to allocate resources that are useful for learning the target's properties in order to recommend more relevant ones in the future. In this paper, we study a simple two-stage recommendation combining a sequential and a batch solution together. We first model the problem with the partially observable Markov decision process (POMDP) and provide an exact solution. Then, through an in-depth analysis over the POMDP value iteration solution, we identify that an exact solution can be abstracted as selecting resources that are not only highly relevant to the target according to the initial-stage information, but also highly correlated, either positively or negatively, with other potential resources for the next stage. With this finding, we propose an approximate solution to ease the intractability of the exact solution. Our initial results on synthetic data and the Movie Lens 100K dataset confirm the performance gains of our theoretical development and analysis

Recent Developments in Recommender Systems: A Survey

In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field

RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems

Towards Scalable Personalization

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most relevant items to their users. The most prominent challenges underlying personalization are: scalability, privacy, and heterogeneity. Scalability is challenging given the growing rate of the Internet and its dynamics, both in terms of churn (i.e., users might leave/join at any time) and changes of user interests over time. Privacy is also a major concern as users might be reluctant to expose their profiles to unknown parties (e.g., other curious users), unless they have an incentive to significantly improve their navigation experience and sufficient guarantees about their privacy. Heterogeneity poses a major technical difficulty because, to be really meaningful, the profiles of users should be extracted from a number of their navigation activities (heterogeneity of source domains) and represented in a form that is general enough to be leveraged in the context of other applications (heterogeneity of target domains). In this dissertation, we address the above-mentioned challenges. For scalability, we introduce democratization and incrementality. Our democratization approach focuses on iteratively offloading the computationally expensive tasks to the user devices (via browsers or applications). This approach achieves scalability by employing the devices of the users as additional resources and hence the throughput of the approach (i.e., number of updates per unit time) scales with the number of users. Our incrementality approach deals with incremental similarity metrics employing either explicit (e.g., ratings) or implicit (e.g., consumption sequences for users) feedback. This approach achieves scalability by reducing the time complexity of each update, and thereby enabling higher throughput. We tackle the privacy concerns from two perspectives, i.e., anonymity from either other curious users (user-level privacy) or the service provider (system-level privacy). We strengthen the notion of differential privacy in the context of recommenders by introducing distance-based differential privacy (D2P) which prevents curious users from even guessing any category (e.g., genre) in which a user might be interested in. We also briefly introduce a recommender (X-REC) which employs uniform user sampling technique to achieve user-level privacy and an efficient homomorphic encryption scheme (X-HE) to achieve system-level privacy. We also present a heterogeneous recommender (X-MAP) which employs a novel similarity metric (X-SIM) based on paths across heterogeneous items (i.e., items from different domains). To achieve a general form for any user profile, we generate her AlterEgo profile in a target domain by employing an item-to-item mapping from a source domain (e.g., movies) to a target domain (e.g., books). Moreover, X-MAP also enables differentially private AlterEgos. While X-MAP employs user-item interactions (e.g., ratings), we also explore the possibility of heterogeneous recommendation by using content-based features of users (e.g., demography, time-varying preferences) or items (e.g., popularity, price)

FATREC Workshop on Responsible Recommendation Proceedings

We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible recommendation: ethical considerations in recommendation, bias and discrimination in recommender systems, transparency and accountability, social impact of recommenders, user privacy, and other related concerns. Our goal was to encourage the community to think about how we build and study recommender systems in a socially-responsible manner. Recommendation systems are increasingly impacting people\u27s decisions in different walks of life including commerce, employment, dating, health, education and governance. As the impact and scope of recommendations increase, developing systems that tackle issues of fairness, transparency and accountability becomes important. This workshop was held in the spirit of FATML (Fairness, Accountability, and Transparency in Machine Learning), DAT (Data and Algorithmic Transparency), and similar workshops in related communities. With Responsible Recommendation , we brought that conversation to RecSys

Privacy-preserving recommendation system using federated learning

Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that utilizes homomorphic encryption. This results in a slight decrease in accuracy metrics but leads to greatly increased user-privacy. We also show that performing computations on encrypted gradients barely affects the recommendation performance while ensuring a more secure means of communicating user gradients to and from the global server