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Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr Madhusree, Mohapatra Puspanjali

Foundations of Computing and Decision Sciences

2021

Vol. 46, No. 4

393--421

The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

Modelling Progressive Filtering

Armano G.

Fundamenta Informaticae

2015

Vol. 138, nr 3

285--320

Progressive filtering is a simple way to perform hierarchical classification, inspired by the behavior that most humans put into practice while attempting to categorize an item according to an underlying taxonomy. Each node of the taxonomy being associated with a different category, one may visualize the categorization process by looking at the item going downwards through all the nodes that accept it as belonging to the corresponding category. This paper is aimed at modeling the progressive filtering technique from a probabilistic perspective. As a result, the designer of a system based on progressive filtering should be facilitated in the task of devising, training, and testing it.