دانلود رایگان مقاله انگلیسی یک رویکرد فازی برای مدیریت نویز طبیعی در سیستم های توصیه گر گروه - الزویر 2018

abstract

Information filtering is a key task in scenarios with information overload. Group Recommender Systems (GRSs) filter content regarding groups of users preferences and needs. Both the recommendation method and the available data influence recommendation quality. Most researchers improved group recommendations through the proposal of new algorithms. However, it has been pointed out that the ratings are not always right because users can introduce noise due to factors such as context of rating or user’s errors. This introduction of errors without malicious intentions is named natural noise, and it biases the recommendation. Researchers explored natural noise management in individual recommendation, but few explored it in GRSs. The latter ones apply crisp techniques, which results in a rigid management. In this work, we propose Natural Noise Management for Groups based on Fuzzy Tools (NNMG-FT). NNMG-FT flexibilises the detection and correction of the natural noise to perform a better removal of natural noise influence in the recommendation, hence, the recommendations of a latter GRS are then improved.

5. Conclusions

This paper proposes a natural noise management approach for group recommender systems using fuzzy tools (NNMG-FT). Specifically, NNMG-FT uses fuzzy profiles to characterise the rating tendency of users and items. With this characterisation, ratings that do not follow their corresponding user and item tendency are identified as noisy and, therefore, corrected. NNMG-FT performs two phases of noise correction: the first one follows a global approach, and the second is personalised to the target group. A case study has been performed to compare NNMG-FT with previous natural noise management approaches. The results show that the management of natural noise with our proposal leads to improved results in the majority of evaluation scenarios, which comprise various aggregation approaches, aggregation strategies and group sizes. Moreover, a deeper study of the proposal showed that the improvement of recommendations is general and few groups had a decay in recommendation quality. The study shows that NNMG-FT is beneficial for group recommendation. In order to further improve the NNM in future works, it is worth to study temporal dynamics, which enhance user preference modelling. Consideration of temporal dynamics would help at both detecting more noisy ratings and avoiding false positives, and therefore improve the detection of noise. Future works will also focus on exploring NNM in contextaware scenarios. Context in recommender systems is characterised by its heterogeneity, covering very diverse information sources, such as temporal information, companion, or weather. Moreover, context-awareness leads to a higher sparsity of ratings. Therefore, specific researches are needed to study the particularities of context-aware scenario, in order to characterise natural noise in group recommender systems databases.