Analysis of recommendation algorithms for e-commerce

By Badrul Munir Sarwar, George Karypis, J. Konstan, John Riedl. ACM Conference on Economics and Computation, 2000

2,050 citations recorded by the source corpus.

Subfields: Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/3219f5733e87afbe1481f7f99c0d06c7c9752c31

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Citations in the corpus

Eliciting Informative Feedback: The Peer-Prediction Method (2005, background): “See Breese et al. (1998) and Sarwar et al. (2000) for reviews.”
A Survey of Collaborative Filtering Techniques (2009, methodology): “Userbased top-N recommendation algorithms firstly identify the k most similar users (nearest neighbors) to the active user using the Pearson correlation or vector-space model [9, 27], in which each user is treated as a vector in the m-dimensional item space and the similarities between the active us”
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences (2005, background): “Regardless of its success in many application settings, the CF approach encounters two serious limitations, namely sparsity and scalability [4, 26].”
Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering (2002, neutral): “We addressed the scalability challenge in an earlier work [12], where we showed that forming neighborhoods in the low dimensional eigen-space provided better quality and performance.”
Item-based collaborative filtering recommendation algorithms (2001, methodology): “To explore that we took an algorithmic approach and used Latent Semantic Indexing (LSI) to capture the similarity between users and items in a reduced dimensional space [24, 25].”
A model of a trust-based recommendation system on a social network (2006, background): “Recent research in computer science has dealt with recommendation systems [26,30].”
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering (2004, methodology): “For existing collaborative .ltering approaches that do not explore transitive associations (we refer to them as standard collaborative .ltering), the denser the consumer product matrix, the higher the overall recommendation quality [Sarwar et al. 2000b].”
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering (2016, methodology): “rison with baselines: We compare the OCuLaR algorithm with various interpretable and non-interpretable baseline algorithms: User-based collaborative ﬁltering using a cosine similarity metric (e.g., [38]). Such an algorithm is interpretable because a recommendation can be justiﬁed with a reasoning o”
Evaluating collaborative filtering recommender systems (2004, methodology): “One approach is the F1 metric (Equation 4), which combines precision and recall into a single number The F1 has been used to evaluate recommender systems in [Sarwar et al. 2000a, Sarwar et al. 2000b].”
A personalized recommender system based on web usage mining and decision tree induction (2002, background): “In an e-commerce environment, the most important errors to avoid are false positives, because these errors will lead to angry customers and thus they will be unlike to revisit the site (Sarwar et al., 2000).”
Evaluating recommender systems from the user’s perspective: survey of the state of the art (2012, background): “…more likely he or she is to prefer the support of recommendation technology.1 Previously, research on recommender system evaluation mainly focused on algorithm performance (Herlocker et al. 2004; Adomavicius and Tuzhilin 2005), especially objective prediction accuracy (Sarwar et al. 2000, 2001).”
Improving recommendation lists through topic diversification (2005, background): “User-based CF has been explored in-depth during the last ten years [29, 24, 14] and represents the most popular recom-”
Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (2018, methodology): “Explicitly, to generate a set of recommendations for a user, Sarwar et al. [34] propose the following approach: (1) The full set of rules are filtered to a subset where the antecedent (X) has previously been liked by the user, but the consequent (Y) is unobserved (2) The filtered rules are then…”