A New Hybrid Recommender System Using Dynamic Fuzzy Clustering

In this paper, a new hybrid system is proposed for combining collaborative and content-based approaches that resolves some limitations of them. By the proposed system, the novelty and diversity of recommendations improve remarkably.
Furthermore, the precision and recall of the proposedsystem is slightly less than those of the best existinghybrid system (collaborative via content) so thatemploying this system is justifiable. By this approach, the items that have not been yet rated by any user can be recommended. Collaborative and content-based systems utilized by this work, use a hybrid method based on fuzzy clustering model (fuzzy subtractive clustering) that combines model and memory-based approaches so that its precision is comparable with the precision of the memory-based approach and its scalability is comparable with the scalability of the model-based approach.
Furthermore, in this work, a dynamic fuzzy clustering algorithm was proposed in which a measure is presented to determine the stage at which a complete reclustering is required. By applying this algorithm, the system is able to adapt to the dynamic and changing environment in a much less expensive manner in terms of computation times and resources.

Read the entire paper at: www.profsite.um.ac.ir/~rmonsefi/conferences/baghebani1.pdf

Making Recommendations Better: An Analytic Model for Human-Recommender Interaction

Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language.

Further, by using an analytic process model, HRI
becomes not only descriptive, but also constructive. It
can help with the design and structure of a
recommender system, and it can act as a bridge
between user information seeking tasks and
recommender algorithms.

Read more at:
http://www.grouplens.org/papers/pdf/mcnee-chi06-hri.pdf

Reinforcing the Blockbuster Nature of Media': The Impact of Online Recommenders

This article examines the effect of recommender systems on the diversity of sales.

Two views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity.

Others believe recommenders only reinforce the popularity of already popular products.his can create" rich-get-richer effects" for popular products and vice-versa for unpopular ones, which results in less diversity.

The authors do not disagree that recommenders beat old-school bestseller lists. But they argue that recommenders lead to less diversity in a world where consumers can also use tools like search engines.

Read the entire article at : http://knowledge.wharton.upenn.edu/articlepdf/1818.pdf?CFID=52235728&CFTOKEN=47328682&jsessionid=a830a8fe82ce46682727