%0 Generic %1 Julian2015 %A McAuley, Julian %A Pandey, R. %A Leskovec, J. %D 2015 %J Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %K sentiment-analysis machine-learning Amazon product-reviews related_works AI related_works_benchmark posted_with_chatgpt %R 10.1145/2783258.2783381 %T Inferring Networks of Substitutable and Complementary Products %U https://www.semanticscholar.org/paper/6505a9bd862fafcde0315a87695c504ced6c7df6 %X To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. In economics, these two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Such relationships are essential as they help us to identify items that are relevant to a user's search. Our goal in this paper is to learn the semantics of substitutes and complements from the text of online reviews. We treat this as a supervised learning problem, trained using networks of products derived from browsing and co-purchasing logs. Methodologically, we build topic models that are trained to automatically discover topics from product reviews that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.

@JournalArticle{Julian2015, abstract = {To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. In economics, these two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Such relationships are essential as they help us to identify items that are relevant to a user's search. Our goal in this paper is to learn the semantics of substitutes and complements from the text of online reviews. We treat this as a supervised learning problem, trained using networks of products derived from browsing and co-purchasing logs. Methodologically, we build topic models that are trained to automatically discover topics from product reviews that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.}, added-at = {2023-09-22T12:16:33.000+0200}, author = {McAuley, Julian and Pandey, R. and Leskovec, J.}, biburl = {https://www.bibsonomy.org/bibtex/26296f4becbc9b27759a076c90156e835/tomvoelker}, day = 29, description = {This paper presents a method for inferring networks of substitutable and complementary products from large scale, raw transaction logs, which is a significant contribution to the field of sentiment analysis in the context of Amazon product reviews.}, doi = {10.1145/2783258.2783381}, interhash = {140513b3aefc557f429bfb4fb1eeac4f}, intrahash = {6296f4becbc9b27759a076c90156e835}, journal = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, keywords = {sentiment-analysis machine-learning Amazon product-reviews related_works AI related_works_benchmark posted_with_chatgpt}, month = {6}, timestamp = {2023-09-22T12:16:33.000+0200}, title = {Inferring Networks of Substitutable and Complementary Products}, url = {https://www.semanticscholar.org/paper/6505a9bd862fafcde0315a87695c504ced6c7df6}, year = 2015 }