Intelligent, Item-Based Stereotype Recommender System

Recommender systems (RS) have become key components driving the success of e-commerce, and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity, the vastness of the data, and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RS, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. This work propose a set of methodologies for the automatic generation of stereotypes during the cold-starts. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. Recommender Systems using the primitive metadata features (baseline systems) as well as factorisation-based systems are used as benchmarks for state-of-the-art methodologies to assess the results of the proposed approach under a wide range of recommendation quality metrics. The results demonstrate how such generic groupings of the metadata features, when performed in a manner that is unaware and independent of the user’s community preferences, may greatly reduce the dimension of the recommendation model, and provide a framework that improves the quality of recommendations in the cold start

Improving Cold Start Stereotype-Based Recommendation Using Deep Learning

Recommendation engines constitute a key component of many online platforms. One of the most challenging problems facing a recommender system is that of cold start, namely the recommendation of items from the catalogue to a new unknown user, or the recommendation of newly injected content to existing users. It is established that incorporating metadata describing the item or the user leads to better cold-start performance. Multiple independent findings point to the value of pre-processing the metadata to generate a new set of coordinates to aid the underlying recommendation algorithm; one of such pre-processing techniques, stereotyped features, has been shown to improve standard recommendation algorithms. Deep learning and complex neural networks have also been widely utilized in recent years in recommender systems, but their application and performance benchmarking in cold start scenarios is still a matter of ongoing research. This article reports on the application of deep learning neural networks to the stereotype driven framework for addressing cold start in recommender systems. We discuss the performance using a range of metrics, covering accuracy, and value content of ranked lists but also serendipity and fairness of recommendations, with the latter becoming an important metric and risk factor for the online platform offering the recommendations. Our findings indicate that a multi-layer neural network substantially improves cold start accuracy performance metrics, despite the recommendations displaying worse fairness and serendipity traits. The work discusses which metrics and scenarios still benefit from stereotyping features for the class of more sophisticated deep learning recommender systems

Workflow Management Table of Content

1 INTRODUCTION TO THE SUBJECT................................................................................... 4 1.1 ABSTRACT...............................................................................................................