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

Decategorizing demographically stereotyped users in a semantic recommender system

In the domain of Digital Television (DTV) broadcasting technology, the enhancement of signals features over classic analog signal transmission allows increasing the amount of content available for TV viewers. Recommender Systems (RS) arose as a suitable choice to assist users in the overwhelming task of selecting audiovisual content, however, the cold-start problem normally associated to the lack of information in early RS stages, causes that user stereotyping approaches are employed meanwhile the lack of information in user profiles is overcome. This paper presents an experimental approach aimed to determine the best conditions for which users who were categorized within a determined stereotype during the cold-start stage, could migrate to a new state in which they receive personalized recommendations. Experimental results show that the best condition under the selected demographic stereotyping scheme for this transition is directly related to the number of TV programs that a user has rated while making use of the system.Valparais