Choice of Metrics used in Collaborative Filtering and their Impact on Recommender Systems

The capacity of recommender systems to make correct predictions is essentially determined by the quality and suitability of the collaborative filtering that implements them. The common memory-based metrics are Pearson correlation and cosine, however, their use is not always the most appropriate or sufficiently justified. In this paper, we analyze these two metrics together with the less common mean squared difference (MSD) to discover their advantages and drawbacks in very important aspects such as the impact when introducing different values of k-neighborhoods, minimization of the MAE error, capacity to carry out a sufficient number of predictions, percentage of correct and incorrect predictions and behavior when attempting to recommend the n-best items. The paper lists the results and practical conclusions that have been obtained after carrying out a comparative study of the metrics based on 135 experiments on the MovieLens database of 100,000 ratios

Constructed wetlands: Prediction of performance with case-based reasoning (part B)

The aim of this research was to assess the treatment efficiencies for gully pot liquor of experimental vertical- flow constructed wetland filters containing Phragmites australis (Cav.) Trin. ex Steud. (common reed) and filter media of different adsorption capacities. Six out of 12 filters received inflow water spiked with metals. For 2 years, hydrated nickel and copper nitrate were added to sieved gully pot liquor to simulate contaminated primary treated storm runoff. The findings were analyzed and discussed in a previous paper (Part A). Case-based reasoning (CBR) methods were applied to predict 5 days at 20°C N-Allylthiourea biochemical oxygen demand (BOD) and suspended solids (SS), and to demonstrate an alternative method of analyzing water quality performance indicators. The CBR method was successful in predicting if outflow concentrations were either above or below the thresholds set for water-quality variables. Relatively small case bases of approximately 60 entries are sufficient to yield relatively high predictions of compliance of at least 90% for BOD. Biochemical oxygen demand and SS are expensive to estimate, and can be cost-effectively controlled by applying CBR with the input variables turbidity and conductivity

The design and implementation of composite Collaborative Filtering algorithm for personalized recommendation

A composite collaborative filtering algorithm for personalized recommend will be presented to solve the original Collaborative Filtering algorithm problem including 'None of User Starting' and 'Data Sparsity', and the Spearman rank correlation coefficient will be used as a main correlation coefficient. Top-M commended is going to be used to get the final results in this paper. At last, we will validate that this algorithm is superior to the algorithm of collaborative filtering based on user and the algorithm of collaborative filtering based on item. © 2012 ACADEMY PUBLISHER

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation

Recommender System Based on Semantic Similarity

In electronic commerce, in order to help users to find their favourite products, we essentially need a system to classify the products based on the user's interests and needs to recommend them to the users. For the same reason the recommendation systems are designed to help finding information in large websites. They are basically developed to offer products to the customers in an automated fashion to help them to do conveniently their shopping. The developing of such systems is important since there are often a large number of factors involved in purchasing a product that would make it difficult for the customer to make the best decision. Finding relationship among users and relationships among products are important issue in these systems. One of relations is similarity. Measure similarity among users and products is used in the pure methods for calculating similarity degree. In this paper, semantic similarity is used to find a set of k nearest neighbours to the target user, or target item. Thus, because of incorporating semantic similarity in the proposed recommendation system, from the experimental results, the high accuracy was obtained on private building company dataset in comparison with state-of-the-art recommender systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.393

The role of Artificial Intelligence and distributed computing in IoT applications

[EN]The exchange of ideas between scientists and technicians, from both academic and business areas, is essential in order to ease the development of systems which can meet the demands of today’s society. Technology transfer in this field is still a challenge and, for that reason, this type of contributions are notably considered in this compilation. This book brings in discussions and publications concerning the development of innovative techniques of IoT complex problems. The technical program focuses both on high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 10 chapters were submitted to this book. The editors particularly encouraged and welcomed contributions on AI and distributed computing in IoT applications.Financed by regional government of Castilla y León and FEDER funds