Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 3-2018 Do your friends make you buy this brand?: Modeling social recommendation with topics and brands Minh Duc LUU Singapore Management University, [email protected] Ee Peng LIM Singapore Management University, [email protected] DOI: https://doi.org/10.1007/s10618-017-0535-9 Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research Part of the Databases and Information Systems Commons, Marketing Commons, and the Social Media Commons Citation LUU, Minh Duc and LIM, Ee Peng. Do your friends make you buy this brand?: Modeling social recommendation with topics and brands. (2018). Data Mining and Knowledge Discovery. 32, (2), 287-319. Research Collection School Of Information Systems. Available at: https://ink.library.smu.edu.sg/sis_research/3783 This Journal Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]. Published in Data Mining and Knowledge Discovery, 2017 September, Pages 1-33 http://doi.org/10.1007/s10618-017-0535-9Data Min Knowl Disc DOI 10.1007/s10618-017-0535-9 Do your friends make you buy this brand? Modeling social recommendation with topics and brands Minh-Duc Luu1 · Ee-Peng Lim1 Received: 5 September 2016 / Accepted: 24 July 2017 © The Author(s) 2017 Abstract Consumer behavior and marketing research have shown that brand has sig- nificant influence on product reviews and product purchase decisions. However, there is very little work on incorporating brand related factors into product recommender systems. Meanwhile, the similarity in brand preference between a user and other socially connected users also affects her adoption decisions. To integrate seamlessly the individual and social brand related factors into the recommendation process, we propose a novel model called Social Brand–Item–Topic (SocBIT). As the original SocBIT model does not enforce non-negativity, which poses some difficulty in result interpretation, we also propose a non-negative version, called SocBIT+. Both SocBIT and SocBIT+ return not only user topic interest, but also brand-related user factors, namely user brand preference and user brand-consciousness. The former refers to user preference for each brand, the latter refers to the extent to which a user relies on brand to make her adoption decisions. Our experiments on real-world datasets demon- strate that SocBIT and SocBIT+ significantly improve rating prediction accuracy over state-of-the-art models such as Social Regularization Ma et al. (in: ACM conference on web search and data mining (WSDM), 2011), Recommendation by Social Trust Ensemble Ma et al. (in: ACM conference on research and development in information retrieval (SIGIR), 2009a) and Social Recommendation Ma et al. (in: ACM conference on information and knowledge management (CIKM), 2008), which incorporate only Responsible editor: Fei Wang. B Minh-Duc Luu [email protected] Ee-Peng Lim [email protected] 1 School of Information Systems, Singapore Management University, 80 Stamford Road, Singapore 178902, Singapore 123 M.-D. Luu, E.-P. Lim the social factors. Specifically, both SocBIT and SocBIT+ offer an improvement of at least 22% over these state-of-the-art models in rating prediction for various real- world datasets. Last but not least, our models also outperform the mentioned models in adoption prediction, e.g., they provide higher precision-at-N and recall-at-N. Keywords Brand effect · Adoption · Social recommendation · Probabilistic matrix factorization · Latent factors 1 Introduction Our behaviors of adopting items are determined by several personal factors (e.g., inter- est, budget constraint, and brand preference); social factors (e.g., friends adopting the same items); item factors (e.g., item features and brand) and other external marketing event factors. Modeling how these factors interact with one another as item adoptions occur is an important research problem as these factors can help build more accurate item search and recommendation applications. Ideally, we would like to consider all these factors in a single model. In this work, we address this goal by focusing on modeling item adoptions that can be attributed to brand among other factors. We first define the concept of brand to distinguish it from other attributes of items. We adapt the following definition of brand from American Marketing Association (AMA) dictionary:1 “A brand is a name, term, design, symbol, or any other feature that identifies one seller’s goods or services as distinct from those of other sellers.” In many settings, brands are the sellers or creators of the goods/services themselves.We thus offer a simple brand definition, i.e., a brand is an entity which creates items/services that can be differentiated from the items/services of others. With this definition, brands can not only be companies, but also individuals that are involved in the creation of products and services. For instance, Steven Spielberg is a “brand” among movie directors as he creates blockbuster movies; Richard Branson is a brand as he is known to found successful businesses. It is noteworthy that brand is more than just a simple feature of item like color, size etc. A brand encompasses several aspects, it can be a symbol of quality (e.g. prestigious brands) and style (e.g. fashion brands) or can be both. A brand is also usually associated with customer service. A simple meta-information alone cannot represent all of these. Neither can topic of item. Thus, we believe that modelling brand is much different from modelling meta-information of item and/or modelling topic of item. The presence of brand leads to new challenges in modeling user–item adoptions. Firstly, every user may have her own brand preferences (or awareness) and users decide adopted items considering brands at different degrees. A user may adopt items based on brand preferences only, on topical interest only, or a mixture of both. When a user does not depend on brand, her brand preferences may become unimportant. On the other extreme, another user may adopt items completely based on brand (i.e., a brand conscious user), making the topical interests less important. 1 https://www.ama.org/resources/Pages/Dictionary.aspx?dLetter=B. 123 Do your friends make you buy this brand? The second challenge is brought about by the co-mingling between brand and social connections in adoption decisions. Just as a user’s interest topics could be influenced by the interest topics of socially connected users, the user’s brand preferences could also be influenced by those of socially connected users. Modeling the brand preferences and brand based adoption decisions in the context of social network is therefore essential. 1.1 Research objectives and contributions While there have been extensive works on recommender systems, including traditional matrix factorization based recommenders (Koren 2008; Koren et al. 2009) and social recommenders (Jiang et al. 2012; Ma et al. 2008, 2009a, 2011b), they do not explicitly model brand related factors which co-mingle with social factors. To develop recom- mender systems that can leverage on brands, we need new methods that address the above two challenges. In this paper, we therefore seek to develop new matrix factor- ization based recommendation models that consider brand-related factors and social factors influencing user–item ratings. We also study how well the new method is, in comparison to those without considering brand-related factors. Ideally, we also want the new method to yield interpretable results which help us learn the dependency of users on making adoption decisions using brand. The novel contributions of this work can be summarized as follows: – We propose a novel matrix factorization-based model, called Social Brand–Item– Topic Model (SocBIT) that incorporates both brand factors and social homophily. Other than modeling each user and item topic factors as in existing models (Jiang et al. 2012; Liu et al. 2015; Ma et al. 2008, 2009a, 2011b), SocBIT goes further by assigning each user and item a set of brand factors and learns the brand- consciousness level of users. SocBIT models social homophily by facilitating socially connected users to share similarity in topic and brand factors. – SocBIT does not enforce non-negative constraints on topic factors, which makes it hard to interpret the learnt factors. We therefore propose a non-negative version for SocBIT, called SocBIT+. Our experiments show that SocBIT+ yield interpretable topic and brand related factors while achieving prediction accuracy similar to that of SocBIT. – Our experiments on real-world datasets from Flixster, FourSquare and ACM Digi- tal Library (ACMDL) show that both SocBIT and SocBIT+ significantly improve user–item rating prediction accuracy over state-of-the-art models, namely, SoReg (Ma et al. 2011b), RSTE (Ma et al. 2009a) and SoRec (Ma et al. 2008). The improvement, in terms of Root Mean Square Error, is at least 25.8% on Flixster data, 31.8% on FourSquare data, and 22% on ACMDL data. Our proposed models also achieve better precision-at-N and recall-at-N than the three state-of-the-art models. – Finally, we are able to derive empirical findings using SocBIT+ so as to answer a few research questions regarding our real datasets, namely: (a) what is the pro- portion of brand conscious users in the studied user population? (b) how are brand conscious users different from other users? (c) how are the brands preferred by brand conscious users different from those by other users? 123 M.-D.