Abstract: Bi-clustering of observations and the variables is of interest in many scientific disciplines; In a single set of data matrix it is handled through the singular value decomposition. Here we deal with two sets of variables: Response and predictor sets. We model the joint relationship via regression models and then apply SVD on the coefficient matrix. The sparseness condition is introduced via Group Lasso; the approach discussed here is quite general and is illustrated with an example from Finance.

Abstract: Creating an optimal digital experience for users require providing users desirable content and also delivering these contents in optimal time as user’s experience and interaction taking place. There are multiple metrics and variables that may determine the success of a “user digital experience”. These metrics may include accuracy, computational cost and other variables. Many of these variables may be contradictory to one another (as explained later in this submission) and their importance may depend on the specific application the digital experience optimization may be pursuing. To deal with this intertwined, possibly contradicting and confusing set of metrics, this invention introduces a generalized index entailing all possible metrics and variables - - that may be significant in defining a successful “digital experience design model”. Besides its generalizability, as it may include any metric the marketers or scientists consider to be important, this new index allows the marketers or the scientists to give different weights to the corresponding metrics as the significance of a specific metric may depends on the specific application. This index is very flexible and could be adjusted as the objective of” user digital experience optimization” may change. Here, we use “recommendation” as equivalent to “content providing” throughout the submission. One well known usage of “recommender systems” is in providing contents such as products, ads, goods, network connections, services, and so on. Recommender systems have other wide and broad applications and - - in general - - many problems and applications in AI and machine learning could be converted easily to an equivalent “recommender system” one. This feature increases the significance of recommender systems as an important application of AI and machine learning. The introduction of internet has brought a new dimension on the ways businesses sell their products and interact with their customers. Ubiquity of the web and consequently web applications are soaring and as a result much of the commerce and customer experience are taking place on line. Many companies offer their products exclusively or predominantly online. At the same time, many present and potential customers spend much time on line and thus businesses try to use efficient models to interact with online users and engage them in various desired initiatives. This interaction with online users is crucial for businesses that hope to see some desired outcome such as purchase, conversions of any types, simple page views, spending longer time on the business pages and so on. Recommendation system is one of the main tools to achieve these outcomes. The basic idea of recommender systems is to analyze what is the probability of a desires action by a specific user. Then, by knowing this probability, one can make decision of what initiatives to be taken to maximize the desirable outcomes of the online user’s actions. The types of initiatives could include, promotional initiatives (sending coupons, cash, …) or communication with the customer using all available media venues such as mail, email, online ad, etc. the main goal of recommendation or targeting model is to increase some outcomes such as “conversion rate”, “length of stay on sites”, “number of views” and so on. There are many other direct or indirect metrics influenced by recommender systems. Examples of these could include an increase of the sale of other products which were not the direct goal of the recommendations, an increase the chance of customer coming back at the site, increase in brand awareness and the chance of retargeting the same user at a later time. The Model: Overview At first, we demonstrate the problem we want to address, and we do it by using many models, data sets and multiple metrics. Then, we propose our unified and generalized metric to address the problems we observe in using different multiple and separate metrics. Thus, we use several models and multiple data sets to evaluate our approach. First, we use all these data sets to evaluate performances of the different models using different performance metrics which are “the state of the art”. Then, we are observing the difficulties of any evaluation using these performance metrics. That is because dealing with different performance metrics, which often make contradictory conclusion, it’d be hard to decide which model has the best performance (so to use the model for the targeting campaign in mind). Therefore, we create our performance index which produces a single, unifying performance metric evaluation a targeting model.

Abstract: The Tolerance Near Set theory - is a formal basis for the observation, comparison and classification of objects, and tolerance Nearness Measure (tNM) is a normalized value, that indicates how much two images are similar. This paper aims to present an application of the algorithm that performs the comparison of images based on the value of tNM, so that the similarities between the images are verified with respect to their characteristics, such as Gray Levels and texture attributes extracted using Gray Level Co-occurrence Matrix (GLCM). Images of the center of some selected cities around the world, are compared using tNM, and classified.

Abstract: Heterogeneous information network (HIN), which involves diverse types of data, has been widely used in recommender systems. However, most existing HINs based recommendation methods equally treat different latent features and simply model various feature interactions in the same way so that the rich semantic information cannot be fully utilized. To comprehensively exploit the heterogeneous information for recommendation, in this paper, we propose a Meta-Graph based Attention-aware Recommendation (MGAR) over HINs. First of all, the MGAR utilizes rich meta-graph based latent features to guide the heterogeneous information fusion recommendation. Specifically, in order to discriminate the importance of latent features generated by different meta-graphs, we propose an attention-based feature enhancement model. The model enables useful features and useless features contribute differently to the prediction, thus improves the performance of the recommendation. Furthermore, to holistically exploit the different interrelation of features, we propose a hierarchical feature interaction method which consists three layers of second-order interaction to mine the underlying correlations between users and items. Extensive experiments show that MGAR outperforms the state-of-the-art recommendation methods in terms of RMSE on Yelp and Amazon Electronics.