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Social and Trust-Centric Recommender Systems

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Abstract

With increasing access to social information about users, merchants can directly incorporate social context in collaborative filtering algorithms. Although some of these methods are discussed in Chapter 10, the focus of this chapter is primarily on recommending nodes and links in network settings. Social context is a much broader concept, not only including social (network) links, but also various types of side information, such as tags or folksonomies. Furthermore, the social context can also be understood in a network-agnostic way, as a special case of context-sensitive recommender systems (cf. Chapter 8). The social setting results in a number of human-centric factors, such as trust. When users are aware of the identity of the actors who participate in the feedback process, the trust factor plays an important role. Therefore, the material in this chapter is closely related to that in Chapter 10, but nevertheless it is distinct enough a merit a separate chapter in its own right. In particular, we will study the following aspects of social context in recommender systems:

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Notes

  1. 1.

    Strictly speaking, such a normalization should also be used in traditional matrix factorization, but it is often omitted on a heuristic basis. In the particular case of trust-centric systems, normalization becomes more important because of the varying sizes of the ratings matrix and trust matrix.

  2. 2.

    Ratings do not always lie in (0, 1). If needed, the ratings matrix can be scaled using its ranges to \((r_{ij} - r_{min})/(r_{max} - r_{min})\), so that all its entries lie in (0, 1).

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    Charu C. Aggarwal

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Aggarwal, C.C. (2016). Social and Trust-Centric Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_11

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