Research commentary on recommendations with side information: A survey and research directions

Abstract

Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.

Research directions for sharing economy issues

“…Nevertheless, sharing has a transformational impact on the related markets for products and services, just like shared cars have an impact on the car sales market (Guo et al, 2019). But this also impacts all of the issues that are affected by the usage of that product or service too, 19 For a sampler of CS research that illustrates the "tip of the iceberg" of these interests, see the following articles: Dillahunt et al (2017); Ghosh et al (2017); Grbovic (2017); Guo et al (2018a); Sun et al (2019;and von Hoffen, 2017). Similarly, representative research conducted in the OR and AI research communities includes: Chow et al (2015); Ghosh et al (2016Ghosh et al ( , 2017; Jia et al, 2017;He and Shen (2015); Nair and Miller-Hooks (2011); and Schuijbroek et al (2017).…”

An Attention-Based Latent Information Extraction Network (ALIEN) for High-Order Feature Interactions

“…Attributes-driven latent information extraction layer (USER × CANDIDATE): This layer performs a macro task of learning the latent information from modeling the low and high-order feature interactions, thereby establishing the contribution of each feature interaction to the user u's interest. Modeling low and high-order feature interactions from user and item attributes is overlooked by many recommendation models [7,10]. In addition, in their models, user and item embeddings u and v are initialized only with indices of u and v, which have vague meanings and converge slowly.…”

“…The user's attributes include demographic factors, e.g., age, gender, occupation and educational background [7,[10][11][12][13][14]. In addition, item attributes such as the category of a product, the genre of a movie and the release date of an album, not only render the basic information about the item, but also provide clues as to why the user is interested in it [10,15]. For example, it is reasonable to recommend Toy Story, a famous cartoon movie, to an eight-year-old boy Peter when he enters a video streaming website.…”

“…(1) is a randomly initialized matrix which is applied to replace QK in the synthesizer (random) method. Therefore, given Equations (10) and (11), F u,v c in random + vanilla mode is:…”

TSCMF: Temporal and social collective matrix factorization model for recommender systems

“…The aforementioned methods exploit only a single type of user-item interaction (users' rating information) without any side information. Exploiting the side information of users or items (Sun et al 2019) beside the users' rating information can help to alleviate the data sparsity problem and thus provides users with better-personalized recommendations (Pan 2016). In this regard, a series of studies based on MF exploit the side information in temporal recommendation systems.…”