Temporal models in recommender systems: an exploratory study on different evaluation dimensions
Year: 2011
Type: dissertation
Abstract: A Recommender System (RS) is a computer program able to identify specific objects for different user interests.Given that many RS have been operating for years, temporal information as a source to obtain better recommendations is acquiring more importance.The currently active research field of RS, has tried to incorporate this information in the form of new recommendation algorithms.However, a common evaluation framework for testing improvements on this area is still missing; most proposals have been developed for and tested under specific (and different) datasets, circumstances and metrics, making it difficult to fairly compare them.This work aims to help establishing a better perspective of the impact of techniques that deal with temporal information on RS.An extensive review of published work on the subject was carried out, including not only time aware techniques, but also the basic techniques upon which time aware extensions are built.A considerable proportion of the techniques under study and metrics for their evaluation were implemented, and a rigorous evaluation protocol scheme was developed, in order to allow the usage of the different techniques under a common experimental setting, which included two common recommendation tasks (rating prediction and top-N recommendation).We assessed the recommendations' results obtained with the different implemented recommendation algorithms on five different evaluation dimensions (statistical accuracy, decision support accuracy, novelty, diversity and coverage), including six different metrics (RMSE, Precision, AUC, Self-Information, Intra List Similarity and Interest Coverage).Results show that, differently to what could be expected, not all time-aware algorithms were able to outperform their time-unaware counterparts, in particular with respect to accuracy on rating prediction (statistical accuracy), which is somewhat unexpected given that, in general, the main motivation for the elaboration of such extensions is accuracy increase.Moreover, the behavior of algorithms on the assessed metrics varies notably, and no particular technique can be considered as "the best" across the different evaluation dimensions.These findings remarks 1) the importance of establishing a common and rigorous evaluation scheme when different algorithms are to be compared and 2) the most suitable recommendation algorithm will depend on the particular task at hand and evaluation dimension of interest.To finalize, we identify interesting research topics related to this work, some of which we hope to address in the near future.each ݅ א ࣣ whose utility is unknown a-priori:where ݎ ො ݅,ݑ is the predicted utility (predicted rating).3 Here used involves several different senses or activities like, for example, seen, heard, tasted, purchased, etc. depending on the type of the item being recommended.
Authors: Campos Soto, Greicilene Regina Pedro
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2299440971