Abstract
Recommender systems (RSs) 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 and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, 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. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. 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. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model.
Highlights
The taxonomy and applications of recommender systems (RSs) have been widely studied, ranging from user-based collaborative filtering (CF)(Billsus and Pazzani 1998; Breese et al 1998; Goldberg et al 1992, 2001; Herlocker et al 1999, 2000, 2002, 2004), to the alternative approach of content-based filtering (CBF) (Deshpande and Karypis 2004; Linden et al 2003; Lops et al 2011; Sarwar et al 2001, 2002)
The stereotype construction procedure applied to the numerical features of the MovieLens/IMDb dataset identified seven features of Type I and seven features of Type II
The table reports the following: (1) the feature mode; (2) the barcode, expressed as a probability value that was attached to that mode if any was identified; (3) the fraction of the population that the mode is deemed to represent; and (4) the lower and upper bounds associated with each stereotype
Summary
The taxonomy and applications of recommender systems (RSs) have been widely studied, ranging from user-based collaborative filtering (CF)(Billsus and Pazzani 1998; Breese et al 1998; Goldberg et al 1992, 2001; Herlocker et al 1999, 2000, 2002, 2004), to the alternative approach of content-based filtering (CBF) (Deshpande and Karypis 2004; Linden et al 2003; Lops et al 2011; Sarwar et al 2001, 2002). During the cold-start phases, when there is little or no feedback for an item or a user, one can resort to finding similarities in the metadata of the new user (item) and the existing users (items). In this research we study the possibility of improving RS performance during cold start by adopting a different point of view from those of previous works, that of rating agnostic stereotypes. In particular the stereotypes stability and their ability to capture user-to-item preference traits This last characteristic is deemed important but it is often overlooked in techniques that are viewed as black boxes and RS driven by deep learning. We wish to gather whether the stereotypes learned have the ability to represent user preferences, in a way that is independent from assessing recommendations.
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