Do recommender systems benefit users? a modeling approach

Do recommender systems benefit users?

Recommender systems are present in many web applications to guide our choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products is questionable. Here we introduce a model to examine the benefit of recommender systems for users, and found that recommendations from the system can be equivalent to random draws if one relies too strongly on ...

Evaluation of recommender systems: A multi-criteria decision making approach

The evaluation and selection of recommender systems is a difficult decision making process. This difficulty is partially due to the large diversity of published evaluation criteria in addition to lack of standardized methods of evaluation. As such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find...

Modeling mutual feedback between users and recommender systems

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this fe...

Evil Twins: Modeling Power Users in Attacks on Recommender Systems

A Contextual Modeling Approach to Context-Aware Recommender Systems

Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper proposes a novel type of contextual modeling (CM) based on the contextual neighbors approach and introduces four specific contextual neighbors methods. It compares these four types of contextual neighbors techniques to determine the best-perf...