Abstract

With the continuous growth of the web, personalized recommender systems（PRS）have been the important building blocks of many online web applications, which contribute to our daily lives in various manners. For example, the product recommendation engines in E-commerce websites recommend potentially interesting products to users, friend recommendation helps to find and connect users in social networks, video recommendation in video sharing websites help users to find favourite videos more quickly and efficiently, and news recommendation in news portals push the latest news to users according to their personalized information needs. In a way, personalized recommendation has become one of the most basic supportive techniques in the era of web intelligence.

Although personalized recommendation has been investigated for decades of years, the wide adoption of latent factor models（LFM）has made the explainability of recommendations an important and critical issue to both the research community and practical application of recommender systems. For example, the algorithm just provide a personalized item recommendation list to the users in many practical systems, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability. The main contributions are as follows.

First, data explainability. Data input is the first step of typical recommender systems, and user-item rating matrix is the most basic data format for most personalized recommendation algorithms, especially for matrix factorization（MF）-based approaches. In this work, we propose localized matrix factorization（LMF）framework based bordered block diagonal form（BBDF）matrices, and further applied this technique for parallelized matrix factorization. Traditional MF algorithms treat the original rating matrix as a whole for factorization, without specific understanding of the inherent structure embedded therein. In this work, however, we propose the（recursive）BBDF structure of sparse matrices, and formally prove its equivalence with community detection on bipartite graphs, with which to explain the inherent community structures and their relationships in sparse matrices. Based on this, we further propose the LMF framework, and prove its compatibility with most of the traditional MF algorithms, which makes it a unified parallelization framework for matrix factorization, that improves both the effect and efficiency at the same time.

Second, model explainability. Based on user-item rating matrices, personalized recommendation algorithms attempt to model user preferences and make personalized recommendations. In this work, we propose explicit factor models（EFM）based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. For their prediction accuracy and scalability, latent factor models（LFM）based on MF have achieved wide application in real-world systems. However, due to their inherently latent factors, it is usually difficult for LFM to provide intuitively understandable explanations to the recommendation algorithms and results, which reduces the persuasiveness of recommendations. In this work, we extract product features and user opinions towards different features from largescale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. Because user preference on features may change over time, we conduct dynamic user modeling based on time series analysis, so as to construct explainable dynamic recommendations.

Third, economic explainability. Based on data analysis and user preference modeling, recommender systems actually manipulate the way that items are matched with users, and eventually affect the economic benefits of the online economic system. In this work, we propose the total surplus maximization（TSM）framework for personalized recommendation, as well as the model specification in different types of online applications. More and more human activities are experiencing the continuous progressing from offline to online, and many commonly used online applications can be formalized into the“producer-service-consumer”framework. For example, in E-commerce websites online retailers（producers）provide normal goods（services）, and the users（consumers）thus make choices and purchases from the vast amount of online services. Based on basic economic concepts, we provide the definitions of utility, cost and surplus in the application scenario of web services, and propose the general framework of web total surplus calculation and maximization. Further more, we specific the total surplus maximization framework to different types of online applications, i.e., E-commerce, P2P lending and online freelancing services. Experimental results on real-world datasets verify that our TSM framework is able to improve the recommendation performance and at the same time benefit the social good of the web.

Key words: personalized recommendation; collaborative filtering; sentiment analysis; explainability; computational economics; artificial intelligence