Inferring preference correlations from social networks Export

@article{hogg:preference, abstract = {Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer's network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products.}, author = {Hogg, Tad}, citeulike-article-id = {4445119}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.elerap.2009.04.006}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1567422309000209}, day = {22}, doi = {10.1016/j.elerap.2009.04.006}, issn = {15674223}, journal = {Electronic Commerce Research and Applications}, keywords = {advertising, clustering, social-networks}, month = {April}, posted-at = {2009-04-30 11:11:53}, priority = {2}, title = {Inferring preference correlations from social networks}, url = {http://dx.doi.org/10.1016/j.elerap.2009.04.006}, year = {2009} }

TY - JOUR ID - hogg:preference L3 - citeulike-article-id:4445119 N2 - Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer's network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products. JF - Electronic Commerce Research and Applications SN - 15674223 TI - Inferring preference correlations from social networks KW - advertising KW - clustering KW - social-networks AU - Hogg, Tad PY - 2009/April/22/ UR - http://dx.doi.org/10.1016/j.elerap.2009.04.006 ER -