Revealing Implicit Communities to Incorporate into Recommender Systems

ing with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. EC’14, June 8–12, 2014, Stanford, CA, USA. Copyright c © 2014 ACM 978-1-4503-2565-3/12/06 . $15.00. http://dx.doi. org/10.1145/2600057.2602906 relationships. The key challenge when applying social systems, however, is to determine which connections should be employed. Explicit social networks may often be ineffective because established friendships are frequently irrelevant to personal preferences. Two individuals may friend one-another on Facebook because they have taken the same math class, but that relationship has no bearing on their taste in film. Moreover, many influential application platforms that would benefit from social recommendations such as Amazon, CNet, and Urbanspoon, do not permit users to state explicit social relationships, and it is difficult, if not impossible, to import relationships from other sources; and privacy concern arises when we integrate commercial systems with social networks. Our goal in this research is to address these limitations by determining how we can identify meaningful relationships through user connections on existing, non-social, platforms. We have observed that many influential recommender systems such as Amazon and Yelp permit users both to post item reviews and to respond to prior reviews. Users may reply to a review for a variety of reasons (e.g. signifying agreement or disagreement or seeking additional information). From these connections we can extract implicit relationships which can in turn be extended to implicit communities. In this paper we will focus on extracting relationships of this type from the Amazon platform. In this work we consider the act of replying to a user to be an interaction between two users: the reviewer who authored the initial review; and the commenter who authors the reply. While it is initially tempting to treat each such interaction as a sign of a meaningful relationship, many such replies are likely to occur by chance. Users will frequently browse for an item of interest, read a review, and post a simple comment without considering the author of the original post. Thus most such interactions can be modeled as a random process. It is necessary to filter out these random interactions in order to provide high-quality personalized guidance. Our goal is thus to develop a rigorous and systematic mechanism to identify significant and non-random connections between users based upon their reviewer-commenter interactions which can then be used to improve the existing recommender systems. There are several fundamental questions that need to be investigated in this work: (1) Do significant non-random connections exist in practice, and can we separate them from the meaningless relationships? (2) Given a pair of users, how can we quantify the strength of their relationship if any and identify any meaningful connections? (3) Once implicit communities are discovered, will they have any relevance to recommender systems or, indeed, any relationship to the individual users’ preferences? We begin by proposing a probabilistic approach to measure the strength and randomness of reviewer-commenter interactions. We will then apply this measure to show that strong non-random relationships exist between Amazon users. We call these extracted connections implicit relationships as opposed to the explicit social relationships found in typical social networks such as Facebook. We will then show that such implicit relationships can provide a direct indication of mutual interests, and can thus be used to substantially improve recommendation accuracy over existing collaborative filtering techniques that only take into account user reviews of common items. Our main contributions are summarized as follows. — We develop a random graph model that represents users as vertices and reviewer-commenter interactions as edges. The graph allows us to distinguish between random and non-random interactions and to study the pattern of real user interactions. If all user interactions occur by chance then real user data will closely match the random graph. If, however, the user data deviates substantially from this model then meaningful relationships are likely to be found. — We develop a quick global indicator that can be used to highlight when, and to what extent, interactions among users deviate from the random model. We observe that interactions in subjective item categories (e.g. book and movie reviews) tend to deviate substantially from the random model while objective categories (e.g. electronics and tools) tend to have more random interactions. — We propose a probabilistic mechanism to quantify the strength of connections between a pair of users based upon the random model which we use to construct a series of relationship graphs. — We apply existing social recommender algorithms using the extracted relationship graphs and show that the resulting recommendations are more accurate than those produced by existing collaborative filtering methods. — Our probabilistic approach allows to adjust trade-off between recommendation accuracy and coverage. We show that discovered communities defined by different strength can be used for hybrid recommender systems to improve both recommendation accuracy and coverage. The remainder of this paper is organized as follows. In Section 2, we review related work. Section 3 presents an abstract model of recommender systems, and introduces basic concepts and notations used through this paper. In Section 4, we describe how we collected data for our experiments and present a few characteristics of this dataset. Section 5 offers results to test the randomness of each user’s interaction in Amazon, our definition of a community, and discovered communities in dataset. In Section 6, we delineate how to utilize discovered communities for the purpose of recommendation and discuss experimental results. Finally, Section 7 concludes the paper.