Incorporating Diversity in a Learning to Rank Recommender System
Venue: FLAIRS Conference

Regularisation is typically applied to the optimisation objective of matrix factorisation methods in order to avoid overfitting. In this paper, we explore the use of regularisation to enhance the diversity of the recommendations produced by these methods. Given a matrix of pairwise item distances, we add regularisation terms dependent on the item distances to the accuracy objective of a learning to rank matrix factorisation formulation. We examine the impact of these regularisers on the latent factors produced by the algorithm and show that such regularisation does indeed promote diversity. The regularisation comes at a cost of performance in terms of accuracy and ultimately the approach cannot greatly enhance diversity without a consequent fall off in accuracy.  Recommender systems have become ubiquitous in online systems and services. Their goal is to help address the choice overload problem, by filtering a large set of possible selections into a much smaller set of recommended items that a user is likely to be interested in. Recommendations are generally based on a set of implicit or explicit item ratings gathered from users in past interactions. Matrix factorisation has proven an effective means of producing accurate recommendations. In such methods, the rating matrix is factored into two low rank matrices, representing userand item latent factors and predicted ratings are obtained by multiplying the corresponding user and item factors. Interest in promoting the diversity of recommendations has increased in recent years. In general, the promotion of diversity is in opposition to the requirement of high accuracy. Many offline studies have found that the more diverse a recommendation is, the less likely it is to match the user's preference and conversely, a highly accurate set is likely to consist of many similar recommendations. Some work, e.g. (Ekstrand et al. 2014) has found positive correlations between diversity and accuracy as subjectively perceived by users in user trials. To date, many of the approaches to diversity enhancement have been developed in the context of memory based algorithms, or alternatively, diversity enhancement has been considered as a separate post processing step carried out after the initial rating predictions have been obtained. In this paper, we Copyright c (c) 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org) All rights reserved. explore whether it is possible to tackle the accuracy and diversity problems together in a single training phase. Regularisation has been used in matrix factorisation algorithms, principally to control for overfitting, by constraining the size of the latent factors. However, in the literature on recommender systems, different types of regularisers have been proposed in order to incorporate other side information into the objective function optimisation to support the recommendation. For example, in (Jamali and Ester 2010) a social regularisation is proposed to incorporate social network information into the optimisation, by encouraging users who are close in the social network to have similar user latent factors. We are motivated by such work to consider whether appropriate regularisation can be used to enhance diversity. Given an item distance matrix, in this paper, we propose a number of regularisations that use the distance matrix to encourage the optimisation to produce factors that result in a diverse set of items in the recommendation list. We evaluate the regularisation methods on two datasets. Ultimately, we observe that, while some of the proposed regularisers are effective in promoting diversity, the diversity cannot be largely improved without a consequent fall off in accuracy. The paper is organised as follows: after reviewing the state of the art and summarising the learning to rank method upon which our regularisers are applied, we propose a number of different regularisation terms and discuss their likely effectiveness. We then describe how to incorporate such regularisation in an alternating least squares optimisation framework. In the evaluation section, we test the regularisers and compare their performance.