Best-fit mobile recharge pack recommendation
28 Mar 2013-pp 1-5
TL;DR: An adaptive recommendation model is discussed about which overcomes various deficiencies associated with existing solutions and recommends suitable recharge packs to subscribers based on their usage history and affordability.
Abstract: In today's competitive market, mobile service providers are very keen on improving the customer satisfaction by providing personalized services. Recommending recharge packs to the subscribers that suits their personal profile is an important such personalized service. But a solution to this problem is not that simple as it requires careful analysis of the subscribers' usage behavior and involves very large volume of data generated by the subscribers' frequent interaction with the telecom network. Also, this solution needs to ensure a fine balance between customer satisfaction and profitability of service providers. This paper discusses about an adaptive recommendation model which overcomes various deficiencies associated with existing solutions. The model recommends suitable recharge packs to subscribers based on their usage history and affordability. Further, it accommodates a configurable fairness parameter that ensures a balance between the profitability factor, conversion probability and relevance of the recommendations. Due to the sheer volume of the data involved, the model is implemented using a distributed framework. The validity of the model is evaluated on the basis of statistical properties and conversion factor.
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01 Jan 2014
TL;DR: This work proposes a new approach based on the multi-armed bandit algorithms to automatically recommend rate-plans for new users in the Telco industry, showing promising results.
Abstract: Recommending best-fit rate-plans for new users is a challenge for the Telco industry. Rate-plans differ from most traditional products in the way that a user normally only have one product at any given time. This, combined with no background knowledge on new users hinders traditional recommender systems. Many Telcos today use either trivial approaches, such as picking a random plan or the most common plan in use. The work presented here shows that these methods perform poorly. We propose a new approach based on the multi-armed bandit algorithms to automatically recommend rate-plans for new users. An experiment is conducted on two different real-world datasets from two brands of a major international Telco operator showing promising results.
5 citations
Cites background from "Best-fit mobile recharge pack recom..."
..., who describe how to recommend best-fit recharges for pre-paid users [8]....
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01 Nov 2019
TL;DR: Interestingly, top 10 impacting recharge products were found to be low cost and, with less validity, promising the rise in long-term revenue owing to increased daily-engagement with the customers by providing next best offers to the telecom prepaid customer.
Abstract: Telecom companies offer a variety of services and products to stay relevant in the competing market. However, with the vast bouquet of products/services, it becomes difficult for the customer to choose the best-fit product. The current research analyzed two recommendation frameworks i.e., An Adaptive Cognizance (AC) distance based algorithms (i.e. Cosine, Euclidean and Manhattan) and Bayesian Network (BN). Various experiments were carried out to evaluate the performance of AC and BN by comparing them with the existing recommendation system by the Telecom service provider (ES). Both AC and BN could uplift the revenue and conversion in the short term by more than 15%. Interestingly, top 10 impacting recharge products were found to be low cost and, with less validity, promising the rise in long-term revenue owing to increased daily-engagement with the customers by providing next best offers to the telecom prepaid customer.
Cites background from "Best-fit mobile recharge pack recom..."
...According to literature, most commonly, the relevant products for each user in recommendation system can be identified by measuring the distance between the usage vector and the product vector calculated using Euclidean distance [6]....
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TL;DR: In this article , the authors compared two distinct recommendation frameworks: a single algorithm and an ensemble algorithm model, and found that the ensemble algorithm-based recommendation engine has proven to provide better recommendations in comparison to individual algorithms.
Abstract: In the realm of computer science, RSS is a set of tools and methods for making useful product recommendations to end users. To maintain footholds in competitive industry, telecoms provide a wide range of offerings. It is challenging for a client to choose the best-fit product from the huge bouquet of products available. It is possible to increase suggestion quality by using the large amounts of textual contextual data detailing item qualities which are accessible with rating data in various recommender’s domains. Users have a hard time making purchases in the telecom industry. Here, fresh strategy for improving recommendation systems in the telecommunications industry is proposed. Users may choose the recommended services which is loaded onto their devices. Using a recommendation engine is a simple way for telecoms to increase trust and customer satisfaction index. The suggested recommendation engine allows users to pick and choose services they need. The present study compared two distinct recommendation frameworks: a single algorithm and an ensemble algorithm model. Experiments were conducted to compare the efficacy of separate algorithms and ensemble algorithm. Interestingly, the ensemble algorithm-based recommendation engine has proven to provide better recommendations in comparison to individual algorithms.
References
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06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.
Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.
Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.
20,309 citations
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
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TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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12,243 citations
"Best-fit mobile recharge pack recom..." refers background in this paper
...If k = 1, then the object is simply assigned to the class of its nearest neighbor [6] [7]....
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TL;DR: In this paper, the strategic parameters have been studied in order to determine the ways in which mobile service providers acquire new customers, and the dependent variable is the service subscribers' intention to switch to a new service provider with personalized services.
Abstract: There has been a notable increase in consumer use of mobile applications. Consumers begin to adopt mobile commerce applications. In response, firms have been investing billions of dollars in order to enhance the hardware and software platforms for mobile commerce. Consequently, with such large investments, firms are highly motivated to attract new clients and retain their old customers. In the present study, the strategic parameters have been studied in order to determine the ways in which mobile service providers acquire new customers. For the purpose of analysis, the dependent variable is the service subscribers' intention to switch to a new service provider with personalized services. Four main constructs have been studied - the amount and the perceived usefulness of general advertisements, the perceived usefulness and privacy issues about personalized advertisements. This empirical study indicates that all four constructs are significant in affecting the decision by subscribers to change to a new mobile service provider.
147 citations
27 Mar 2006
TL;DR: SMMART is a context-aware, adaptive and personalized m-commerce application designed to deliver targeted promotions to the users of mobile devices about the products they like while guarding the users’ identity and protecting them from any unsolicited messages.
Abstract: Unique features of handheld devices, including their mobility, personalization and location-awareness engender new types of applications for mobile commerce, such as mobile advertising. Mobile marketing and advertising applications deliver promotional information to consumers based on their preferences and location. In this paper, we present SMMART, a context-aware, adaptive and personalized m-commerce application designed to deliver targeted promotions to the users of mobile devices about the products they like while guarding the users’ identity and protecting them from any unsolicited messages. Promotions distributed by SMMART are personalized by performing intelligent matching of the user’s shopping interests to current promotions available at a retail site. SMMART can adapt to changing preferences of its user by inconspicuously monitoring his or her shopping habits. We describe a fully functional prototype of SMMART built for Pocket PCs running Windows CE with .NET Compact Framework. This paper also presents a study demonstrating end-user usability and economic viability of SMMART.
140 citations
"Best-fit mobile recharge pack recom..." refers background in this paper
...One of the major focus areas for the mobile service provider in terms of ensuring customer satisfaction and increase in revenue is to provide more and more personalized services to the subscriber [1][2]....
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