A probabilistic model to resolve diversity-accuracy challenge of recommendation systems

Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity-accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy-diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure

Optimization of Media Strategy via Marketing Mix Modeling in Retailing

 The paper describes the marketing mix modeling results for companies in nonfood retailing. The main objectives of the research are to demonstrate the viable way of making effective recommendations for optimizing the media strategy by modeling offline and online traffic to the stores based on econometric modeling and to develop a decision support system, which enhance the effective growth of business KPIs and an effective decision-making process. Econometric modeling, deeper data analysis, decision support were implemented on the data of one of the main retailers in Ukraine in a period before the full-scale Russian invasion. Estimating the impact of different communication channels on business results made basis for ROI calculations and optimization of media investments allocation among media channels by periods, video durations, type of advertising and with optimal weekly media pressure. ROMI calculation was based on the results of regression modeling, which estimate the level of traffic and sales generated by each media channel. The information-analytical decision support system based on an interactive dashboard has been developed for improvement of day-by-day business planning and management. The developed framework of regional strategy selection facilitates to the formation of a strategic vision on a regional scale and improves the quality of a regional media strategy

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

Estimating truncated hotel demand: A comparison of low computational cost forecasting methods

The aim of this thesis is to evaluate the effectiveness of six selected low computational cost hotel demand forecasting methods (SA, SMA, EMA, DEMA, BP and PU) in terms of restoring truncated demand data, and then identify a low-cost and easy to follow demand forecasting method that can be used by U.S. independent hotels. Obtaining revenue gains by applying demand forecasting techniques have been proved by many studies in hospitality and other related industries. However, few studies have focused on low computational forecasting methods\u27 comparison in hospitality field. For this reason, the author decided to test the performance of six selected demand forecasting techniques, with the aim of identifying an effective method for hotels operators constrained by financial resources and expertise. This thesis first simulates leisure and business real demand booking curves under a pre-decided increasing rate in each of three leisure/business ratio scenarios (1:3, 1:1, and 3:1). In the second stage, true demands are truncated in three cases. They are 1) capacity truncation, 2) 50% truncation of total business demand, and 3) 25% truncation of total business demand. And then, six selected forecasting methods are applied to the truncated demand. Finally, the forecasting accuracy for each method is evaluated in both statistical and economical models. The results of the experiment indicate that PU method outperform all the other selected methods and was proved to be the most effective forecasting method for U.S. independent hotels. Other new findings include that the data restoration accuracy ranged from a negative relationship with the business demand proportion of total bookings, and the higher the percentage the business bookings were truncated, the smaller the detruncation error occurs. The results also shows that the less the business booking was truncated; the more variable the forecasting error occurs. An interesting finding of this thesis is that in some specific circumstances, the results of statistical evaluation do not completely in accordance with economical evaluation results