Gamification as a new concept uses game elements in a novel way to engage users of a non-gaming system and can be used in many domains within an enterprise, to implement the organizational processes with lower costs, higher quality or in a more efficient way. Although there are many researches on gamification but a few studies can be found in the organizational gamification and there are few research works about framework and methodology for designing and implementing organizational gamification in the literature. The purpose of this article is to provide a comprehensive methodology for the enterprise gamification. This research is an attempt to overcome the mentioned gap via presenting a methodology by applying some important issues including organizational, humanity and gamification aspects together to design and implement customized enterprise gamification solutions through reviewing the related literature and experts’ commentaries. The evaluation of the methodology showed that it is an appropriate and perfect way to design gamification solutions in an organization, besides the enterprise needs to provide the necessary conditions for its implementation. This paper forwards an important debate on a comprehensive methodology for applying enterprise gamification, which explains how to properly use gamification in enterprises to increase productivity and better communication with employees, and thus contributes to literature on internal and enterprise gamification.

Bidding strategy is an important part of a negotiation strategy in automated multi-issue negotiations. In order to present good offers, which help maximize the agent’s utility, we need to search the outcome space and find appropriate bids. Bid search can become challenging in large outcome spaces with more than ten thousands of possible bids. The traditional search methods such as exhaustive or binary search are not efficient enough to find the right bids in a large space. This is mostly due to the high number of issues, high number of possible values for each issue, and increased time complexity of usual search methods. In this paper, we investigate the potential of using meta-heuristic methods for optimizing bid search in large outcome spaces. We apply some of the most popular meta-heuristic algorithms for bid search in bidding strategy of baseline negotiating agents and evaluate their impacts on negotiation performance in different negotiation domains. The evaluation results obtained through comprehensive experiments show how meta-heuristic algorithms can help improve bid search capability and consequently negotiation performance of the agents on different performance criteria. In addition, we show which search algorithm is most suitable for improving any particular performance criterion.

One of the most important solutions for dimensionality reduction in data preprocessing, and improving classification performance is gene selection in microarray data since they usually have several thousand genes with very few samples. Because of these characteristics, the complexity of classification models increases and their efficiency decreases. The gene selection problem inherently pursues two goals: reducing the number of genes and increasing the classification efficiency. Therefore, this paper presents a novel hybrid filter-wrapper solution based on the Fisher-score method and Multi-Objective Forest Optimization Algorithm (MOFOA). In the proposed method, as a preprocessing step, the Fisher-score method selects 500 discriminative genes by removing redundant/irrelevant genes. Then, MOFOA searches to find the subset of optimal genes using concepts such as repository, crowding-distance, and binary tournament selection. Moreover, the proposed method solves the gene selection problem and, at the same time, optimizes the kernel parameters in the SVM classification model. Six microarray datasets were used to evaluate the performance of the proposed method. Afterward, a comparison was made between its results and those of the four multi-objective hybrid methods presented in the literature in terms of classification performance, the number of selected genes, running time, and hypervolume criteria. According to the results, in addition to selecting fewer genes, the proposed solution has achieved greater classification accuracy in most cases and has been able to obtain a performance similar to or better than that of other multi-objective gene selection approaches.

Nowadays, companies offer product bundles with special discounts in order to sell more products. However, it is important to note that customers show different levels of loyalties to companies, and each segment of the market has unique features, which influences the customers’ buying patterns. The primary purpose of this paper is to propose a novel model for product bundling in e-commerce websites by using market segmentation variables and customer loyalty analysis. RFM model is employed to calculate customer loyalty. Subsequently, the customers are grouped based on their loyalty levels. Each group is then divided into different segments based on market segmentation variables. The product bundles are determined for each market segment via clustering algorithms. Apriori algorithm is also used to determine the association rules for each product bundle. Classification models are applied in order to determine which product bundle should be recommended to each customer. The results demonstrate that the silhouette coefficient, support, confidence, and accuracy values are higher when both customer loyalty level and market segmentation variables are used in product bundling. Accordingly, the proposed model increases the chance of success in direct marketing and recommending product bundles to customers.

Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users’ interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.

Recommender systems help users faced with the problem of information overflow and provide personalized recommendations. Social networks are used for providing variety of business or social activities, or sometimes a combination of both. In this paper, by considering social network of users and according to users’ context and items, a new method is introduced that is based on trust and context aware for recommender systems in social networks. The purpose of this paper is to create a recommender system which increases precision of predicted ratings for all users especially for cold start users. In the proposed method, walking on web of trust is done by neighbor users for finding rating of similar items and users’ preference is gotten of items’ context. The results show that suitable recommendation with user’s context is provided by using this method. Also, this system can increase precision of predicted rating for all users and cold starts too and however, do not decrease the rating’s coverage.

In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.

Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative filtering and content-based filtering. In this study, a recommender system model based on collaborative filtering has proposed. Proposed model was endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. This improvement was done using time context and group preferences. Experimental results show that the proposed model has a better recommendation performance than existing models.

One of the primary issues on marketing planning is to know the customer & apos; s behavioral trends. A
customer & apos; s purchasing interest may fluctuate for different reasons and it is important to find the
declining or increasing trends whenever they happen. It is important to study these fluctuations
to improve customer relationships. There are different methods to increase the customer & apos; s
willingness such as planning good promotions, an increase on advertisement, etc. This paper
proposes a new methodology to measure customer & apos; s behavioral trends called customer
electrocardiogram. The proposed model of this paper uses K-means clustering method with
RFM analysis to study customer & apos; s fluctuations over different time frames. We also apply the
proposed electrocardiogram methodology for a real-world case study of food industry and the
results are discussed in details